Category Archives: Risk Management

When the Tools We Embrace Become the Tools They Exploit — AI and Automation in the Cybersecurity Arms Race

Posted on by
Reply

Introduction
We live in a world of accelerating change, and nowhere is that more evident than in cybersecurity operations. Enterprises are rushing to adopt AI and automation technologies in their security operations centres (SOCs) to reduce mean time to detect (MTTD), enhance threat hunting, reduce cyber­alert fatigue, and generally eke out more value from scarce resources. But in parallel, adversaries—whether financially motivated cybercriminal gangs, nation‑states, or hacktivists—are themselves adopting (and in some cases advancing) these same enabling technologies. The result: a moving target, one where the advantage is fleeting unless defenders recognise the full implications, adapt processes and governance, and invest in human‑machine partnerships rather than simply tool acquisition.

A digital image of a brain thinking 4684455

In this post I’ll explore the attacker/defender dynamics around AI/automation, technology adoption challenges, governance and ethics, how to prioritise automation versus human judgement, and finally propose a roadmap for integrating AI/automation into your SOC with realistic expectations and process discipline.

1. Overview of Attacker/Defender AI Dynamics

The basic story is: defenders are trying to adopt AI/automation, but threat actors are often moving faster, or in some cases have fewer constraints, and thus are gaining asymmetric advantages.

Put plainly: attackers are weaponising AI/automation as part of their toolkit (for reconnaissance, social engineering, malware development, evasion) and defenders are scrambling to catch up. Some of the specific offensive uses: AI to craft highly‑persuasive phishing emails, to generate deep‑fake audio or video assets, to automate vulnerability discovery and exploitation at scale, to support lateral movement and credential stuffing campaigns.

For defenders, AI/automation promises faster detection, richer context, reduction of manual drudge work, and the ability to scale limited human resources. But the pace of adoption, the maturity of process, the governance and skills gaps, and the need to integrate these into a human‑machine teaming model mean that many organisations are still in the early innings. In short: the arms race is on, and we’re behind.

2. Key Technology Adoption Challenges: Data, Skills, Trust

As organisations swallow the promise of AI/automation, they often underestimate the foundational requirements. Here are three big challenge areas:

a) Data

  • AI and ML need clean, well‑structured data. Many security operations environments are plagued with siloed data, alert overload, inconsistent taxonomy, missing labels, and legacy tooling. Without good data, AI becomes garbage‑in/garbage‑out.

  • Attackers, on the other hand, are using publicly available models, third‑party tools and malicious automation pipelines that require far less polish—so they have a head start.

b) Skills and Trust

  • Deploying an AI‑powered security tool is only part of the solution. Tuning the models, understanding their outputs, incorporating them into workflows, and trusting them requires skilled personnel. Many SOC teams simply don’t yet have those resources.

  • Trust is another factor: model explainability, bias, false positives/negatives, adversarial manipulation of models—all of these undermine operator confidence.

c) Process Change vs Tool Acquisition

  • Too many organisations acquire “AI powered” tools but leave underlying processes, workflows, roles and responsibilities unchanged. The tool then becomes a silos‑in‑a‑box rather than a transformational capability.

  • Without adjusted processes, organisations can end up with “alert‑spam on steroids” or AI acting as a black box forcing humans to babysit again.

  • In short: People and process matter at least as much as technology.

3. Governance & Ethics of AI in Cyber Defence

Deploying AI and automation in cyber defence doesn’t simply raise technical questions — it raises governance and ethics questions.

  • Organisations need to define who is accountable for AI‑driven decisions (for example a model autonomously taking containment action), how they audit and validate AI output, how they respond if the model is attacked or manipulated, and how they ensure human oversight.

  • Ethical issues include: (i) making sure model biases don’t produce blind spots or misclassifications; (ii) protecting privacy when feeding data into ML systems; (iii) understanding that attackers may exploit the same models or our systems’ dependence on them; and (iv) ensuring transparency where human decision‑makers remain in the loop.

A governance framework should address model lifecycle (training, validation, monitoring, decommissioning), adversarial threat modeling (how might the model itself be attacked), and human‑machine teaming protocols (when does automation act, when do humans intervene).

4. Prioritising Automation vs Human Judgement

One of the biggest questions in SOC evolution is: how do we draw the line between automation/AI and human judgment? The answer: there is no single line — the optimal state is human‑machine collaboration, with clearly defined tasks for each.

  • Automation‑first for repetitive, high‑volume, well‑defined tasks: For example, triage of alerts, enrichment of IOC/IOA (indicators/observables), initial containment steps, known‑pattern detection. AI can accelerate these tasks, free up human time, and reduce mean time to respond.

  • Humans for context, nuance, strategy, escalation: Humans bring judgement, business context, threat‑scenario understanding, adversary insight, ethics, and the ability to handle novel or ambiguous situations.

  • Define escalation thresholds: Automation might execute actions up to a defined confidence level; anything below should escalate to a human analyst.

  • Continuous feedback loop: Human analysts must feed back into model tuning, rules updates, and process improvement — treating automation as a living capability, not a “set‑and‑forget” installation.

  • Avoid over‑automation risks: Automating without oversight can lead to automation‑driven errors, cascading actions, or missing the adversary‑innovation edge. Also, if you automate everything, you risk deskilling your human team.

The right blend depends on your maturity, your toolset, your threat profile, and your risk appetite — but the underlying principle is: automation should augment humans, not replace them.

5. Roadmap for Successful AI/Automation Integration in the SOC

  1. Assess your maturity and readiness

  2. Define use‑cases with business value

  3. Build foundation: data, tooling, skills

  4. Pilot, iterate, scale

  5. Embed human‑machine teaming and continuous improvement

  6. Maintain governance, ethics and risk oversight

  7. Stay ahead of the adversary

(See main post above for in-depth detail on each step.)

Conclusion: The Moving Target and the Call to Action

The fundamental truth is this: when defenders pause, attackers surge. The race between automation and AI in cyber defence is no longer about if, but about how fast and how well. Threat actors are not waiting for your slow adoption cycles—they are already leveraging automation and generative AI to scale reconnaissance, craft phishing campaigns, evade detection, and exploit vulnerabilities at speed and volume. Your organisation must not only adopt AI/automation, but adopt it with the right foundation, the right process, the right governance and the right human‑machine teaming mindset.

At MicroSolved we specialise in helping organisations bridge the gap between technological promise and operational reality. If you’re a CISO, SOC manager or security‑operations leader who wants to –

  • understand how your data, processes and people stack up for AI/automation readiness

  • prioritise use‑cases that drive business value rather than hype

  • design human‑machine workflows that maximise SOC impact

  • embed governance, ethics and adversarial AI awareness

  • stay ahead of threat actors who are already using automation as a wedge into your environment

… then we’d welcome a conversation. Reach out to us today at info@microsolved.com or call +1.614.351.1237and let’s discuss how we can help you move from reactive to resilient, from catching up to keeping ahead.

Thanks for reading. Be safe, be vigilant—and let’s make sure the advantage stays with the good guys.

References

  1. ISC2 AI Adoption Pulse Survey 2025

  2. IBM X-Force Threat Intelligence Index 2025

  3. Accenture State of Cybersecurity Resilience 2025

  4. Cisco 2025 Cybersecurity Readiness Index

  5. Darktrace State of AI Cybersecurity Report 2025

  6. World Economic Forum: Artificial Intelligence and Cybersecurity Report 2025

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Critical Zero Trust Implementation Blunders Companies Must Avoid Now

Posted on by
Reply

 

Introduction: The Urgent Mandate of Zero Trust

In an era of dissolved perimeters and sophisticated threats, the traditional “trust but verify” security model is obsolete. The rise of distributed workforces and complex cloud environments has rendered castle-and-moat defenses ineffective, making a new mandate clear: Zero Trust. This security framework operates on a simple yet powerful principle: never trust, always verify. It assumes that threats can originate from anywhere, both inside and outside the network, and demands that no user or device be granted access until their identity and context are rigorously validated.

ZeroTrustScorecard

The Shifting Security Perimeter: Why Zero Trust is Non-Negotiable

The modern enterprise no longer has a single, defensible perimeter. Data and applications are scattered across on-premises data centers, multiple cloud platforms, and countless endpoints. This new reality is a goldmine for attackers, who exploit implicit trust within networks to move laterally and escalate privileges. This is compounded by the challenges of remote work; research from Chanty shows that 76% of cybersecurity professionals believe their organization is more vulnerable to cyberattacks because of it. A Zero Trust security model directly confronts this reality by treating every access request as a potential threat, enforcing strict identity verification and least-privilege access for every user and device, regardless of location.

The High Stakes of Implementation: Why Avoiding Blunders is Critical

Adopting a Zero Trust framework is not a minor adjustment—it is a fundamental transformation of an organization’s security posture. While the benefits are immense, the path to implementation is fraught with potential pitfalls. A misstep can do more than just delay progress; it can create new security gaps, disrupt business operations, and waste significant investment. Getting it right requires a strategic, holistic approach. Understanding the most common and critical implementation blunders is the first step toward building a resilient and effective Zero Trust architecture that truly protects an organization’s most valuable assets.

Blunder 1: Mistaking Zero Trust for a Product, Not a Strategy

One of the most pervasive and damaging misconceptions is viewing Zero Trust as a single technology or a suite of products that can be purchased and deployed. This fundamentally misunderstands its nature and sets the stage for inevitable failure.

The Product Pitfall: Believing a Single Solution Solves All

Many vendors market their solutions as “Zero Trust in a box,” leading organizations to believe that buying a specific firewall, identity management tool, or endpoint agent will achieve a Zero Trust posture. This product-centric view ignores the interconnectedness of users, devices, applications, and data. No single vendor or tool can address the full spectrum of a Zero Trust architecture. This approach often results in a collection of siloed security tools that fail to communicate, leaving critical gaps in visibility and enforcement.

The Strategic Imperative: Developing a Comprehensive Zero Trust Vision

True Zero Trust is a strategic framework and a security philosophy that must be woven into the fabric of the organization. It requires a comprehensive vision that aligns security policies with business objectives. This Zero Trust strategy must define how the organization will manage identity, secure devices, control access to applications and networks, and protect data. It is an ongoing process of continuous verification and refinement, not a one-time project with a clear finish line.

Avoiding the Trap: Actionable Steps for a Strategic Foundation

To avoid this blunder, organizations must begin with strategy, not technology. Form a cross-functional team including IT, security, networking, and business leaders to develop a phased roadmap. This plan should start by defining the most critical assets and data to protect—the “protect surface.” From there, map transaction flows, architect a Zero Trust environment, and create dynamic security policies. This strategic foundation ensures that technology purchases serve the overarching goals, rather than dictating them.

Blunder 2: Skipping Comprehensive Inventory and Underestimating Scope

A core principle of Zero Trust is that you cannot protect what you do not know exists. Many implementation efforts falter because they are built on an incomplete or inaccurate understanding of the IT environment. Diving into policy creation without a complete asset inventory is like trying to secure a building without knowing how many doors and windows it has.

The “Unknown Unknowns”: Securing What You Don’t See

Organizations often have significant blind spots in their IT landscape. Shadow IT, forgotten legacy systems, unmanaged devices, and transient cloud workloads create a vast and often invisible attack surface. Without a comprehensive inventory of all assets—including users, devices, applications, networks, and data—it’s impossible to apply consistent security policies. Attackers thrive on these “unknown unknowns,” using them as entry points to bypass security controls.

The Scope Illusion: Underestimating All Connected Workloads and Cloud Environments

The scope of a modern enterprise network extends far beyond the traditional office. It encompasses multi-cloud environments, SaaS applications, IoT devices, and API-driven workloads. Underestimating this complexity is a common mistake. A Zero Trust strategy must account for every interconnected component. Failing to discover and map dependencies between these workloads can lead to policies that either break critical business processes or leave significant security vulnerabilities open for exploitation.

Avoiding the Trap: The Foundational Importance of Discovery and Continuous Asset Management

The solution is to make comprehensive discovery and inventory the non-negotiable first step. Implement automated tools that can continuously scan the environment to identify and classify every asset. This is not a one-time task; it must be an ongoing process of asset management. This complete and dynamic inventory serves as the foundational data source for building effective network segmentation, crafting granular access control policies, and ensuring the Zero Trust architecture covers the entire digital estate.

Blunder 3: Neglecting Network Segmentation and Micro-segmentation

For decades, many organizations have operated on flat, highly permissive internal networks. Once an attacker breaches the perimeter, they can often move laterally with ease. Zero Trust dismantles this outdated model by assuming a breach is inevitable and focusing on containing its impact through rigorous network segmentation.

The Flat Network Fallacy: A Breadth-First Attack Vector

A flat network is an attacker’s playground. After gaining an initial foothold—often through a single compromised device or set of credentials—they can scan the network, discover valuable assets, and escalate privileges without encountering significant barriers. This architectural flaw is responsible for turning minor security incidents into catastrophic data breaches. Relying on perimeter defense alone is a failed strategy in the modern threat landscape.

The Power of Micro-segmentation: Isolating Critical Assets

Micro-segmentation is a core tenet of Zero Trust architecture. It involves dividing the network into small, isolated zones—sometimes down to the individual workload level—and enforcing strict access control policies between them. If one workload is compromised, the breach is contained within its micro-segment, preventing the threat from spreading across the network. This granular control dramatically shrinks the attack surface and limits the blast radius of any security incident.

Avoiding the Trap: Designing Granular Access Controls

To implement micro-segmentation effectively, organizations must move beyond legacy VLANs and firewall rules. Utilize modern software-defined networking and identity-based segmentation tools to create dynamic security policies. These policies should enforce the principle of least privilege, ensuring that applications, workloads, and devices can only communicate with the specific resources they absolutely require to function. This approach creates a resilient network where lateral movement is difficult, if not impossible.

Blunder 4: Overlooking Identity and Access Management Essentials

In a Zero Trust framework, identity is the new perimeter. Since trust is no longer granted based on network location, the ability to robustly authenticate and authorize every user and device becomes the cornerstone of security. Failing to fortify identity management practices is a fatal flaw in any Zero Trust initiative.

The Weakest Link: Compromised Credentials and Privileged Accounts

Stolen credentials remain a primary vector for major data breaches. Weak passwords, shared accounts, and poorly managed privileged access create easy pathways for attackers. An effective identity management program is essential for mitigating these risks. Without strong authentication mechanisms and strict controls over privileged accounts, an organization’s Zero Trust ambitions will be built on a foundation of sand.

The Static Access Mistake: Assuming Trust After Initial Authentication

A common mistake is treating authentication as a one-time event at the point of login. This “authenticate once, trust forever” model is antithetical to Zero Trust. A user’s context can change rapidly: they might switch to an unsecure network, their device could become compromised, or their behavior might suddenly deviate from the norm. Static trust models fail to account for this dynamic risk, leaving a window of opportunity for attackers who have hijacked an active session.

Avoiding the Trap: Fortifying Identity Security Solutions

A robust Zero Trust strategy requires a mature identity and access management (IAM) program. This includes enforcing strong, phishing-resistant multi-factor authentication (MFA) for all users, implementing a least-privilege access model, and using privileged access management (PAM) solutions to secure administrative accounts. Furthermore, organizations must move toward continuous, risk-based authentication, where access is constantly re-evaluated based on real-time signals like device posture, location, and user behavior.

Blunder 5: Ignoring Third-Party Access and Supply Chain Risks

An organization’s security posture is only as strong as its weakest link, which often lies outside its direct control. Vendors, partners, and contractors are an integral part of modern business operations, but they also represent a significant and often overlooked attack vector.

The Extended Attack Surface: Vendor and Supply Chain Vulnerabilities

Every third-party vendor with access to your network or data extends your attack surface. These external entities may not adhere to the same security standards, making them prime targets for attackers seeking a backdoor into your organization. In fact, a staggering 77% of all security breaches originated with a vendor or other third party, according to a Whistic report. Ignoring this risk is a critical oversight.

Lax Access Control for External Entities: A Gateway for Attackers

Granting vendors broad, persistent access—often through traditional VPNs—is a recipe for disaster. This approach provides them with the same level of implicit trust as an internal employee, allowing them to potentially access sensitive systems and data far beyond the scope of their legitimate needs. If a vendor’s network is compromised, that access becomes a direct conduit for an attacker into your environment.

Avoiding the Trap: Strict Vetting and Granular Controls

Applying Zero Trust principles to third-party access is non-negotiable. Begin by conducting rigorous security assessments of all vendors before granting them access. Replace broad VPN access with granular, application-specific access controls that enforce the principle of least privilege. Each external user’s identity should be strictly verified, and their access should be limited to only the specific resources required for their role, for the minimum time necessary.

Blunder 6: Disregarding User Experience and Neglecting Security Awareness

A Zero Trust implementation can be technically perfect but fail completely if it ignores the human element. Security measures that are overly complex or disruptive to workflows will inevitably be circumvented by users focused on productivity.

The Friction Fallout: User Workarounds and Shadow IT Resurgence

If security policies introduce excessive friction—such as constant, unnecessary authentication prompts or blocked access to legitimate tools—employees will find ways around them. This can lead to a resurgence of Shadow IT, where users adopt unsanctioned applications and services to get their work done, creating massive security blind spots. A successful Zero Trust strategy must balance security with usability.

The Human Firewall Failure: Lack of Security Awareness Training

Zero Trust is a technical framework, but it relies on users to be vigilant partners in security. Without proper training, employees may not understand their role in the new model. They may fall for sophisticated phishing attacks, which have seen a 1,265% increase driven by GenAI, unknowingly providing attackers with the initial credentials needed to challenge the Zero Trust defenses.

Avoiding the Trap: Empowering Users with Secure Simplicity

Strive to make the secure path the easy path. Implement solutions that leverage risk-based, adaptive authentication to minimize friction for low-risk activities while stepping up verification for sensitive actions. Invest in continuous security awareness training that educates employees on new threats and their responsibilities within the Zero Trust framework. When users understand the “why” behind the security policies and find them easy to follow, they become a powerful asset rather than a liability.

Blunder 7: Treating Zero Trust as a “Set It and Forget It” Initiative

The final critical blunder is viewing Zero Trust as a project with a defined endpoint. The threat landscape, technology stacks, and business needs are in a constant state of flux. A Zero Trust architecture that is not designed to adapt will quickly become obsolete and ineffective.

The Static Security Stagnation: Failing to Adapt to Threat Landscape Changes

Attackers are constantly evolving their tactics. A security policy that is effective today may be easily bypassed tomorrow. A static Zero Trust implementation fails to account for this dynamic reality. Without continuous monitoring, analysis, and refinement, security policies can become stale, and new vulnerabilities in applications or workloads can go unnoticed, creating fresh gaps for exploitation. Furthermore, the integration of automation is crucial, as organizations using security AI can identify and contain a data breach 80 days faster than those without.

Conclusion

Successfully implementing a Zero Trust architecture is a transformative journey that demands strategic foresight and meticulous execution. The path is challenging, but by avoiding these critical blunders, organizations can build a resilient, adaptive security posture fit for the modern digital era.

The key takeaways are clear:

  • Embrace the Strategy: Treat Zero Trust as a guiding philosophy, not a checklist of products. Build a comprehensive roadmap before investing in technology.
  • Know Your Terrain: Make complete and continuous inventory of all assets—users, devices, workloads, and data—the absolute foundation of your initiative.
  • Isolate and Contain: Leverage micro-segmentation to shrink your attack surface and prevent the lateral movement of threats.
  • Fortify Identity: Make strong, adaptive identity and access management the core of your security controls.
  • Balance Security and Usability: Design a framework that empowers users and integrates seamlessly into their workflows, supported by ongoing security awareness.
  • Commit to the Journey: Recognize that Zero Trust is an iterative, ongoing process of refinement and adaptation, not a one-time project.

By proactively addressing these potential pitfalls, your organization can move beyond legacy security models and chart a confident course toward a future where trust is never assumed and every single access request is rigorously verified.

Contact MicroSolved, Inc. for More Information or Assistance

For expert guidance on implementing a resilient Zero Trust architecture tailored to your organization’s unique needs, consider reaching out to the experienced team at MicroSolved, Inc. With decades of experience in information security and a proven track record of helping companies navigate complex security landscapes, MicroSolved, Inc. offers valuable insights and solutions to enhance your security posture.

  • Phone: Reach us at +1.614.351.1237
  • Email: Drop us a line at info@microsolved.com
  • Website: Visit our website at www.microsolved.com for more information on our services and expertise.

Our team of seasoned experts is ready to assist you at any stage of your Zero Trust journey, from initial strategy development to continuous monitoring and refinement. Don’t hesitate to contact us for comprehensive security solutions that align with your business goals and operational requirements.

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

 

 

Securing AI / Generative AI Use in the Enterprise: Risks, Gaps & Governance

Posted on by
Reply

Imagine this: a data science team is evaluating a public generative AI API to help with summarization of documents. One engineer—trying to accelerate prototyping—uploads a dataset containing customer PII (names, addresses, payment tokens) without anonymization. The API ingests that data. Later, another user submits a prompt that triggers portions of the PII to be regurgitated in an output. The leakage reaches customers, regulators, and media.

This scenario is not hypothetical. As enterprise adoption of generative AI accelerates, organizations are discovering that the boundary between internal data and external AI systems is porous—and many have no governance guardrails in place.

VendorRiskAI

According to a recent report, ~89% of enterprise generative AI usage is invisible to IT oversight—that is, it bypasses sanctioned channels entirely. Another survey finds that nearly all large firms deploying AI have seen risk‑related losses tied to flawed outputs, compliance failures, or bias.

The time to move from opportunistic pilots toward robust governance and security is now. In this post I map the risk taxonomy, expose gaps, propose controls and governance models, and sketch a maturity roadmap for enterprises.

Risk Taxonomy

Below I classify major threat vectors for AI / generative AI in enterprise settings.

1. Model Poisoning & Adversarial Inputs

  • Training data poisoning: attackers insert malicious or corrupted data into the training set so that the model learns undesirable associations or backdoors.

  • Backdoor / trigger attacks: a model behaves normally unless a specific trigger pattern (e.g. a token or phrase) is present, which causes malicious behavior.

  • Adversarial inputs at inference time: small perturbations or crafted inputs cause misclassification or manipulation of model outputs.

  • Prompt injection / jailbreaking: an end user crafts prompts to override constraints, extract internal context, or escalate privileges.

2. Training Data Leakage

  • Sensitive training data (proprietary IP, PII, trade secrets) may inadvertently be memorized by large models and revealed via probing.

  • Even with fine‑tuning, embeddings or internal layers might leak associations that can be reverse engineered.

  • Leakage can also occur via model updates, snapshots, or transfer learning pipelines.

3. Inference-Time Output Attacks & Leakage

  • Model outputs might infer relationships (e.g. “given X, the missing data is Y”) that were not explicitly in training but learned implicitly.

  • Large models can combine inputs across multiple queries to reconstruct confidential data.

  • Malicious users can sample outputs exhaustively or probe with adversarial prompts to elicit sensitive data.

4. Misuse & “Shadow AI”

  • Shadow AI: employees use external generative tools outside IT visibility (e.g. via personal ChatGPT accounts) and paste internal documents, violating policy and leaking data.

  • Use of unconstrained AI for high-stakes decisions without validation or oversight.

  • Automation of malicious behaviors (fraud, social engineering) via internal AI capabilities.

5. Compliance, Privacy & Governance Risks

  • Violation of data protection regulations (e.g. GDPR, CCPA) via improper handling or cross‑boundary transfer of PII.

  • In regulated industries (healthcare, finance), AI outputs may inadvertently produce disallowed inferences or violate auditability requirements.

  • Lack of explainability or audit trails makes it hard to prove compliance or investigate incidents.

  • Model decisions may reflect bias, unfairness, or discriminatory patterns that trigger regulatory or reputational liabilities.

Gaps in Existing Solutions

  • Traditional security tooling is blind to AI risks: DLP, EDR, firewall rules do not inspect semantic inference or prompt-based leakage.

  • Lack of visibility into model internals: Most deployed models (especially third‑party or foundation models) are black boxes.

  • Sparse standards & best practices: While frameworks exist (NIST AI RMF, EU AI Act, ISO proposals), concrete guidance for securing generative AI in enterprises is immature.

  • Tooling mismatch: Many AI governance tools are nascent and do not integrate smoothly with existing enterprise security stacks.

  • Team silos: Data science, DevOps, and security often operate in silos. Defects emerge at the intersection.

  • Skill and resource gaps: Few organizations have staff experienced in adversarial ML, formal verification, or privacy-preserving AI.

  • Lifecycle mismatch: AI models require continuous retraining, drift detection, versioning—traditional security is static.

Governance & Defensive Strategies

Below are controls, governance practices, and architectural strategies enterprises should consider.

AI Risk Assessment / Classification Framework

  • Inventorize all AI / ML assets (foundation models, fine‑tuned models, inference APIs).

  • Classify models by risk tier (e.g. low / medium / high) based on sensitivity of inputs/outputs, business criticality, and regulatory impact.

  • Map threat models for each asset: e.g. poisoning, leakage, adversarial use.

  • Integrate this with enterprise risk management (ERM) and vendor risk processes.

Secure Development & DevSecOps for Models

  • Embed adversarial testing, fuzzing, red‑teaming in model training pipelines.

  • Use data validation, anomaly detection, outlier filtering before ingesting training data.

  • Employ version control, model lineage, and reproducibility controls.

  • Build a “model sandbox” environment with strict controls before production rollout.

Access Control, Segmentation & Audit Trails

  • Enforce least privilege access for training data, model parameters, hyperparameters.

  • Use role-based access control (RBAC) and attribute-based access (ABAC) for model execution.

  • Maintain full audit logging of prompts, responses, model invocations, and guardrails.

  • Segment model infrastructure from general infrastructure (use private VPCs, zero trust).

Privacy / Sanitization Techniques

  • Use differential privacy to add noise and limit exposure of individual records.

  • Use secure multiparty computation (SMPC) or homomorphic encryption for sensitive computations.

  • Apply data anonymization / tokenization / masking before use.

  • Use output filtering / content policies to supersede model outputs that might leak or violate policy.

Monitoring, Anomaly Detection & Runtime Guardrails

  • Monitor model outputs for anomalies, drift, suspicious prompting patterns.

  • Use “canary” prompts or test probes to detect model corruption or behavior shifts.

  • Rate-limit or throttle requests to model endpoints.

  • Use AI-defense systems to detect prompt injection or malicious patterns.

  • Flag or block high-risk output paths (e.g. outputs that contain PII, internal config, backdoor triggers).

Operational Integration

Security–Data Science Collaboration

  • Embed security engineers in the AI development lifecycle (shift-left).

  • Educate data scientists in adversarial ML, model risks, privacy constraints.

  • Use cross-functional review boards for high-risk model deployments.

Shadow AI Discovery & Mitigation

  • Monitor outbound traffic or SaaS logins for generative AI usage.

  • Use SaaS monitoring tools or proxy policies to intercept and flag unsanctioned AI use.

  • Deploy internal tools or wrappers for generative AI that inject audit controls.

  • Train employees and publish acceptable use policies for AI usage.

Runtime Controls & Continuous Testing

  • Periodically red-team models (both internal and third-party) to detect vulnerabilities.

  • Revalidate models after each update or retrain.

  • Set up incident response plans specific to AI incidents (model rollback, containment).

  • Conduct regular audits of model behavior, logs, and drift performance.

Case Studies & Real-World Failures & Successes

  • Researchers have found that injecting as few as 250 malicious documents can backdoor a model.

  • Foundation model leakage incidents have been demonstrated in academic research (models regurgitating verbatim input).

  • Organizations like Microsoft Azure, Google Cloud, and OpenAI are starting to offer tools and guardrails (rate limits, privacy options, usage logging) to support enterprise introspection.

  • Some enterprises are mandating all internal AI interactions to flow through a “governed AI proxy” layer to filter or scrub prompts/outputs.

Roadmap / Maturity Model

I propose a phased model:

  1. Awareness & Inventory

    • Catalog AI/ML assets

    • Basic training & policies

    • Executive buy-in

  2. Baseline Controls

    • Access controls, audit logging

    • Data sanitization & DLP for AI pipelines

    • Shadow AI monitoring

  3. Model Protection & Hardening

    • Differential privacy, adversarial testing, prompt filters

    • Runtime anomaly detection

    • Sandbox staging

  4. Audit, Metrics & Continuous Improvement

    • Regular red teaming

    • Drift detection & revalidation

    • Integration into ERM / compliance

    • Internal assurance & audit loops

  5. Advanced Guardrails & Automation

    • Automated policy enforcement

    • Self-healing / rollback mechanisms

    • Formal verification, provable defenses

    • Model explainability & transparency audits

By advancing along this maturity curve, enterprises can evolve from reactive posture to proactive, governed, and resilient AI operations—reducing risk while still reaping the transformative potential of generative technologies.

Need Help or More Information?

Contact MicroSolved and put our deep expertise to work for you in this area. Email us (info@microsolved.com) or give us a call (+1.614.351.1237) for a no-hassle, no-pressure discussion of your needs and our capabilities. We look forward to helping you protect today and predict what is coming next. 

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

The Largest Benefit of the vCISO Program for Clients

Posted on by
Reply

If you’ve been around information security long enough, you’ve seen it all — the compliance-driven checkboxes, the fire drills, the budget battles, the “next-gen” tools that rarely live up to the hype. But after decades of leading MSI’s vCISO team and working with organizations of all sizes, I’ve come to believe that the single largest benefit of a vCISO program isn’t tactical — it’s transformational.

It’s the knowledge transfer.

Not just “advice.” Not just reports. I mean a deep, sustained process of transferring mental models, systems thinking, and tools that help an organization develop real, operational security maturity. It’s a kind of mentorship-meets-strategy hybrid that you don’t get from a traditional full-time CISO hire, a compliance auditor, or a MSSP dashboard.

And when it’s done right, it changes everything.

From Dependency to Empowerment

When our vCISO team engages with a client, the initial goal isn’t to “run security” for them. It’s to build their internal capability to do so — confidently, independently, and competently.

We teach teams the core systems and frameworks that drive risk-based decision making. We walk them through real scenarios, in real environments, explaining not just what we do — but why we do it. We encourage open discussion, transparency, and thought leadership at every level of the org chart.

Once a team starts to internalize these models, you can see the shift:

  • They begin to ask more strategic questions.

  • They optimize their existing tools instead of chasing shiny objects.

  • They stop firefighting and start engineering.

  • They take pride in proactive improvement instead of waiting for someone to hand them a policy update.

The end result? A more secure enterprise, a more satisfied team, and a deeply empowered culture.

ChatGPT Image Sep 3 2025 at 03 06 40 PM

It’s Not About Clock Hours — It’s About Momentum

One of the most common misconceptions we encounter is that a CISO needs to be in the building full-time, every day, running the show.

But reality doesn’t support that.

Most of the critical security work — from threat modeling to policy alignment to risk scoring — happens asynchronously. You don’t need 40 hours a week of executive time to drive outcomes. You need strategic alignment, access to expertise, and a roadmap that evolves with your organization.

In fact, many of our most successful clients get a few hours of contact each month, supported by a continuous async collaboration model. Emergencies are rare — and when they do happen, they’re manageable precisely because the organization is ready.

Choosing the Right vCISO Partner

If you’re considering a vCISO engagement, ask your team this:
Would you like to grow your confidence, your capabilities, and your maturity — not just patch problems?

Then ask potential vCISO providers:

  • What’s your core mission?

  • How do you teach, mentor, and build internal expertise?

  • What systems and models do you use across organizations?

Be cautious of providers who over-personalize (“every org is unique”) without showing clear methodology. Yes, every organization is different — but your vCISO should have repeatable, proven systems that flex to your needs. Likewise, beware of vCISO programs tied to VAR sales or specific product vendors. That’s not strategy — it’s sales.

Your vCISO should be vendor-agnostic, methodology-driven, and above all, focused on growing your organization’s capability — not harvesting your budget.

A Better Future for InfoSec Teams

What makes me most proud after all these years in the space isn’t the audits passed or tools deployed — it’s the teams we’ve helped become great. Teams who went from reactive to strategic, from burned out to curious. Teams who now mentor others.

Because when infosec becomes less about stress and more about exploration, creativity follows. Culture follows. And the whole organization benefits.

And that’s what a vCISO program done right is really all about.

 

* The included images are AI-generated.

Distracted Minds, Not Sophisticated Cyber Threats — Why Human Factors Now Reign Supreme

Posted on by
Reply

Problem Statement: In cybersecurity, we’ve long feared the specter of advanced malware and AI-enabled attacks. Yet today’s frontline is far more mundane—and far more human. Distraction, fatigue, and lack of awareness among employees now outweigh technical threats as the root cause of security incidents.

A woman standing in a room lit by bright fluorescent lights surrounded by whiteboards and sticky notes filled with ideas sketching out concepts and plans 5728491

A KnowBe4 study released in August 2025 sets off alarm bells: 43 % of security incidents stem from employee distraction—while only 17 % involve sophisticated attacks.

1. Distraction vs. Technical Threats — A Face-off

The numbers are telling:

  • Distraction: 43 %

  • Lack of awareness training: 41 %

  • Fatigue or burnout: 31 %

  • Pressure to act quickly: 33 %

  • Sophisticated attack (the myths we fear): just 17 %

What explains the gap between perceived threat and actual risk? The answer lies in human bandwidth—our cognitive load, overload, and vulnerability under distraction. Cyber risk is no longer about perimeter defense—it’s about human cognitive limits.

Meanwhile, phishing remains the dominant attack vector—74 % of incidents—often via impersonation of executives or trusted colleagues.

2. Reviving Security Culture: Avoid “Engagement Fatigue”

Many organizations rely on awareness training and phishing simulations, but repetition without innovation breeds fatigue.

Here’s how to refresh your security culture:

  • Contextualized, role-based training – tailor scenarios to daily workflows (e.g., finance staff vs. HR) so the relevance isn’t lost.

  • Micro-learning and practice nudges – short, timely prompts that reinforce good security behavior (e.g., reminders before onboarding tasks or during common high-risk activities).

  • Leadership modeling – when leadership visibly practices security—verifying emails, using MFA—it normalizes behavior across the organization.

  • Peer discussions and storytelling – real incident debriefs (anonymized, of course) often land harder than scripted scenarios.

Behavioral analytics can drive these nudges. For example: detect when sensitive emails are opened, when copy-paste occurs from external sources, or when MFA overrides happen unusually. Then trigger a gentle “Did you mean to do this?” prompt.

3. Emerging Risk: AI-Generated Social Engineering

Though only about 11 % of respondents have encountered AI threats so far, 60 % fear AI-generated phishing and deepfakes in the near future.

This fear is well-placed. A deepfake voice or video “CEO” request is far more convincing—and dangerous.

Preparedness strategies include:

  • Red teaming AI threats — simulate deepfake or AI-generated social engineering in safe environments.

  • Multi-factor and human challenge points — require confirmations via secondary channels (e.g., “Call the sender” rule).

  • Employee resilience training — teach detection cues (synthetic audio artifacts, uncanny timing, off-script wording).

  • AI citizenship policies — proactively define what’s allowed in internal tools, communication, and collaboration platforms.

4. The Confidence Paradox

Nearly 90 % of security leaders feel confident in their cyber-resilience—yet the data tells us otherwise.

Overconfidence can blind us: we might under-invest in human risk management while trusting tech to cover all our bases.

5. A Blueprint for Human-Centric Defense

Problem Actionable Solution
Engagement fatigue with awareness training Use micro-learning, role-based scenarios, and frequent but brief content
Lack of behavior change Employ real-time nudges and behavioral analytics to catch risky actions before harm
Distraction, fatigue Promote wellness, reduce task overload, implement focus-support scheduling
AI-driven social engineering Test with red teams, enforce cross-channel verification, build detection literacy
Overconfidence Benchmark human risk metrics (click rates, incident reports); tie performance to behavior outcomes

Final Thoughts

At its heart, cybersecurity remains a human endeavor. We chase the perfect firewall, but our biggest vulnerabilities lie in our own cognitive gaps. The KnowBe4 study shows that distraction—not hacker sophistication—is the dominant risk in 2025. It’s time to adapt.

We must refresh how we engage our people—not just with better tools, but with better empathy, smarter training design, and the foresight to counter AI-powered con games.

This is the human-centered security shift Brent Huston has championed. Let’s own it.

Help and More Information

If your organization is struggling to combat distraction, engagement fatigue, or the evolving risk of AI-powered social engineering, MicroSolved can help.

Our team specializes in behavioral analytics, adaptive awareness programs, and human-focused red teaming. Let’s build a more resilient, human-aware security culture—together.

👉 Reach out to MicroSolved today to schedule a consultation or request more information. (info@microsolved.com or +1.614.351.1237)

References

  1. KnowBe4. Infosecurity Europe 2025: Human Error & Cognitive Risk Findings. knowbe4.com

  2. ITPro. Employee distraction is now your biggest cybersecurity risk. itpro.com

  3. Sprinto. Trends in 2025 Cybersecurity Culture and Controls.

  4. Deloitte Insights. Behavioral Nudges in Security Awareness Programs.

  5. Axios & Wikipedia. AI-Generated Deepfakes and Psychological Manipulation Trends.

  6. TechRadar. The Growing Threat of AI in Phishing & Vishing.

  7. MSI :: State of Security. Human Behavior Modeling in Red Teaming Environments.

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Operational Burnout: The Hidden Risk in Cyber Defense Today

Posted on by
Reply

The Problem at Hand

Burnout is epidemic among cybersecurity professionals. A 2024‑25 survey found roughly 44 % of cyber defenders report severe work‑related stress and burnout, while another 28 % remain uncertain whether they might be heading that way arXiv+1. Many are hesitant to admit difficulties to leadership, perpetuating a silent crisis. Nearly 46 % of cybersecurity leaders have considered leaving their roles, underscoring how pervasive this issue has become arXiv+1.

ChatGPT Image Aug 6 2025 at 01 56 13 PM

Why This Matters Now

Threat volumes continue to escalate even as budgets stagnate or shrink. A recent TechRadar piece highlights that 79 %of cybersecurity professionals say rising threats are impacting their mental health—and that trend is fueling operational fragility TechRadar. In the UK, over 59 % of cyber workers report exhaustion-related symptoms—much higher than global averages (around 47 %)—tied to manual monitoring, compliance pressure, and executive misalignmentdefendedge.com+9IT Pro+9ACM Digital Library+9.

The net result? Burned‑out teams make mistakes: missed patches, alert fatigue, overlooked maintenance. These seemingly small lapses pave the way for significant breaches TechRadar.

Root Causes & Stress Drivers

  • Stacked expectations: RSA’s 2025 poll shows professionals often juggle over seven distinct stressors—from alert volume to legal complexity to mandated uptime CyberSN.

  • Tool sprawl & context switching: Managing dozens of siloed security products increases cognitive load, reduces threat visibility, and amplifies fatigue—36 % report complexity slows decision‑making IT Pro.

  • Technostress: Rapid change in tools, lack of standardization, insecurity around job skills, and constant connectivity lead to persistent strain Wikipedia.

  • Organizational disconnect: When boards don’t understand cybersecurity risk in business terms, teams shoulder disproportionate burden with little support or recognition IT Pro+1.

Systemic Risks to the Organization

  • Slower incident response: Fatigued analysts are slower to detect and react, increasing dwell time and damage.

  • Attrition of talent: A single key employee quit can leave high-value skills gaps; nearly half of security leaders struggle to retain key people CyberSN+1.

  • Reduced resilience: Burnout undermines consistency in basic hygiene—patches, training, monitoring—which are the backbone of cyber hygiene TechRadar.

Toward a Roadmap for Culture Change

1. Measure systematically

Use validated instruments (e.g. Maslach Burnout Inventory or Occupational Depression Inventory) to track stress levels over time. Monitor absenteeism, productivity decline, sick-day trends tied to mental health Wikipedia.

2. Job design & workload balance

Apply the Job Demands–Resources (JD‑R) model: aim to reduce excessive demands and bolster resources—autonomy, training, feedback, peer support Wikipedia+1. Rotate responsibilities and limit on‑call hours. Avoid tool overload by consolidating platforms where possible.

3. Leadership alignment & psychological safety

Cultivate a strong psychosocial safety climate—executive tone that normalizes discussion of workload, stress, concerns. A measured 10 % improvement in PSC can reduce burnout by ~4.5 % and increase engagement by ~6 %Wikipedia. Equip CISOs to translate threat metrics into business risk narratives IT Pro.

4. Formal support mechanisms

Current offerings—mindfulness programs, mental‑health days, limited coverage—are helpful but insufficient. Embed support into work processes: peer‑led debriefs, manager reviews of workload, rotation breaks, mandatory time off.

5. Cross-functional support & resilience strategy

Integrate security operations with broader recovery, IT, risk, and HR workflows. Shared incident response roles reduce the silos burden while sharpening resilience TechRadar.

Sector Best Practices: Real-World Examples

  • An international workshop of security experts (including former NSA operators) distilled successful resilience strategies: regular check‑ins, counselor access after critical incidents, and benchmarking against healthcare occupational burnout models arXiv.

  • Some progressive organizations now consolidate toolsets—or deploy automated clustering to reduce alert fatigue—cutting up to 90 % of manual overload and saving analysts thousands of hours annually arXiv.

  • UK firms that marry compliance and business context in cybersecurity reporting tend to achieve lower stress and higher maturity in risk posture comptia.org+5IT Pro+5TechRadar+5.

✅ Conclusion: Shifting from Surviving to Sustaining

Burnout is no longer a peripheral HR problem—it’s central to cyber defense resilience. When skilled professionals are pushed to exhaustion by staffing gaps, tool overload, and misaligned expectations, every knob in your security stack becomes a potential failure point. But there’s a path forward:

  • Start by measuring burnout as rigorously as you measure threats.

  • Rebalance demands and resources inside the JD‑R framework.

  • Build a psychologically safe culture, backed by leadership and board alignment.

  • Elevate burnout responses beyond wellness perks—to embedded support and rotation policies.

  • Lean into cross-functional coordination so security isn’t just a team, but an integrated capability.

Burnout mitigation isn’t soft; it’s strategic. Organizations that treat stress as a systemic vulnerability—not just a personal problem—will build security teams that last, adapt, and stay effective under pressure.

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

CISO AI Board Briefing Kit: Governance, Policy & Risk Templates

Posted on by
Reply

Imagine the boardroom silence when the CISO begins: “Generative AI isn’t a futuristic luxury—it’s here, reshaping how we operate today.” The questions start: What is our AI exposure? Where are the risks? Can our policies keep pace? Today’s CISO must turn generative AI from something magical and theoretical into a grounded, business-relevant reality. That urgency is real—and tangible. The board needs clarity on AI’s ecosystem, real-world use cases, measurable opportunities, and framed risks. This briefing kit gives you the structure and language to lead that conversation.

ExecMeeting

Problem: Board Awareness + Risk Accountability

Most boards today are curious but dangerously uninformed about AI. Their mental models of the technology lag far behind reality. Much like the Internet or the printing press, AI is already driving shifts across operations, cybersecurity, and competitive strategy. Yet many leaders still dismiss it as a “staff automation tool” rather than a transformational force.

Without a structured briefing, boards may treat AI as an IT issue, not a C-suite strategic shift with existential implications. They underestimate the speed of change, the impact of bias or hallucination, and the reputational, legal, or competitive dangers of unmanaged deployment. The CISO must reframe AI as both a business opportunity and a pervasive risk domain—requiring board-level accountability. That means shifting the picture from vague hype to clear governance frameworks, measurable policy, and repeatable audit and reporting disciplines.

Boards deserve clarity about benefits like automation in logistics, risk analysis, finance, and security—which promise efficiency, velocity, and competitive advantage. But they also need visibility into AI-specific hazards like data leakage, bias, model misuse, and QA drift. This kit shows CISOs how to bring structure, vocabulary, and accountability into the conversation.

Framework: Governance Components

1. Risk & Opportunity Matrix

Frame generative AI in a two-axis matrix: Business Value vs Risk Exposure.

Opportunities:

  • Process optimization & automation: AI streamlines repetitive tasks in logistics, finance, risk modeling, scheduling, or security monitoring.

  • Augmented intelligence: Enhancing human expertise—e.g. helping analysts faster triage security events or fraud indicators.

  • Competitive differentiation: Early adopters gain speed, insight, and efficiency that laggards cannot match.

Risks:

  • Data leakage & privacy: Exposing sensitive information through prompts or model inference.

  • Model bias & fairness issues: Misrepresentation or skewed outcomes due to historical bias.

  • Model drift, hallucination & QA gaps: Over- or under-tuned models giving unreliable outputs.

  • Misuse or model sprawl: Unsupervised use of public LLMs leading to inconsistent behaviour.

Balanced, slow-trust adoption helps tip the risk-value calculus in your favor.

2. Policy Templates

Provide modular templates that frame AI like a “human agent in training,” not just software. Key policy areas:

  • Prompt Use & Approval: Define who can prompt models, in what contexts, and what approval workflow is needed.

  • Data Governance & Retention: Rules around what data is ingested or output by models.

  • Vendor & Model Evaluation: Due diligence criteria for third-party AI vendors.

  • Guardrails & Safety Boundaries: Use-case tiers (low-risk to high-risk) with corresponding controls.

  • Retraining & Feedback Loops: Establish schedule and criteria for retraining or tuning.

These templates ground policy in trusted business routines—reviews, approvals, credentialing, audits.

3. Training & Audit Plans

Reframe training as culture and competence building:

  • AI Literacy Module: Explain how generative AI works, its strengths/limitations, typical failure modes.

  • Role-based Training: Tailored for analysts, risk teams, legal, HR.

  • Governance Committee Workshops: Periodic sessions for ethics committee, legal, compliance, and senior leaders.

Audit cadence:

  • Ongoing Monitoring: Spot-checks, drift testing, bias metrics.

  • Trigger-based Audits: Post-upgrade, vendor shift, or use-case change.

  • Annual Governance Review: Executive audit of policy adherence, incidents, training, and model performance.

Audit AI like human-based systems—check habits, ensure compliance, adjust for drift.

4. Monitoring & Reporting Metrics

Technical Metrics:

  • Model performance: Accuracy, precision, recall, F1 score.

  • Bias & fairness: Disparate impact ratio, fairness score.

  • Interpretability: Explainability score, audit trail completeness.

  • Security & privacy: Privacy incidents, unauthorized access events, time to resolution.

Governance Metrics:

  • Audit frequency: % of AI deployments audited.

  • Policy compliance: % of use-cases under approved policy.

  • Training participation: % of staff trained, role-based completion rates.

Strategic Metrics:

  • Usage adoption: Active users or teams using AI.

  • Business impact: Time saved, cost reduction, productivity gains.

  • Compliance incidents: Escalations, regulatory findings.

  • Risk exposure change: High-risk projects remediated.

Boards need 5–7 KPIs on dashboards that give visibility without overload.

Implementation: Briefing Plan

Slide Deck Flow

  1. Title & Hook: “AI Isn’t Coming. It’s Here.”

  2. Risk-Opportunity Matrix: Visual quadrant.

  3. Use-Cases & Value: Case studies.

  4. Top Risks & Incidents: Real-world examples.

  5. Governance Framework: Your structure.

  6. Policy Templates: Categories and value.

  7. Training & Audit Plan: Timeline & roles.

  8. Monitoring Dashboard: Your KPIs.

  9. Next Steps: Approvals, pilot runway, ethics charter.

Talking Points & Backup Slides

  • Bullet prompts: QA audits, detection sample, remediation flow.

  • Backup slides: Model metrics, template excerpts, walkthroughs.

Q&A and Scenario Planning

Prep for board Qs:

  • Verifying output accuracy.

  • Legal exposure.

  • Misuse response plan.

Scenario A: Prompt exposes data. Show containment, audit, retraining.
Scenario B: Drift causes bad analytics. Show detection, rollback, adjustment.

When your board walks out, they won’t be AI experts. But they’ll be AI literate. And they’ll know your organization is moving forward with eyes wide open.

More Info and Assistance

At MicroSolved, we have been helping educate boards and leadership on cutting-edge technology issues for over 25 years. Put our expertise to work for you by simply reaching out to launch a discussion on AI, business use cases, information security issues, or other related topics. You can reach us at +1.614.351.1237 or info@microsolved.com.

We look forward to hearing from you! 

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Continuous Third‑Party Risk: From SBOM Pipelines to SLA Enforcement

Posted on by
Reply

Recent supply chain disasters—SolarWinds and MOVEit—serve as stark wake-up calls. These breaches didn’t originate inside corporate firewalls; they started upstream, where vendors and suppliers held the keys. SolarWinds’ Orion compromise slipped unseen through trusted vendor updates. MOVEit’s managed file transfer software opened an attack gateway to major organizations. These incidents underscore one truth: modern supply chains are porous, complex ecosystems. Traditional vendor audits, conducted quarterly or annually, are woefully inadequate. The moment a vendor’s environment shifts, your security posture does too—out of sync with your risk model. What’s needed isn’t another checkbox audit; it’s a system that continuously ingests, analyzes, and acts on real-world risk signals—before third parties become your weakest link.

ThirdPartyRiskCoin

The Danger of Static Assessments 

For decades, third-party risk management (TPRM) relied on periodic rites: contracts, questionnaires, audits. But those snapshots fail to capture evolving realities. A vendor may pass a SOC 2 review in January—then fall behind on patching in February, or suffer a credential leak in March. These static assessments leave blind spots between review windows.

Point-in-time audits also breed complacency. When a questionnaire is checked, it’s filed; no one revisits until the next cycle. During that gap, new vulnerabilities emerge, dependencies shift, and threats exploit outdated components. As noted by AuditBoard, effective programs must “structure continuous monitoring activities based on risk level”—not by arbitrary schedule AuditBoard.

Meanwhile, new vulnerabilities in vendor software may remain undetected for months, and breaches rarely align with compliance windows. In contrast, continuous third-party risk monitoring captures risk in motion—integrating dynamic SBOM scans, telemetry-based vendor hygiene signals, and SLA analytics. The result? A live risk view that’s as current as the threat landscape itself.

Framework: Continuous Risk Pipeline

Building a continuous risk pipeline demands a multi-pronged approach designed to ingest, correlate, alert—and ultimately enforce.

A. SBOM Integration: Scanning Vendor Releases

Software Bill of Materials (SBOMs) are no longer optional—they’re essential. By ingesting vendor SBOMs (in SPDX or CycloneDX format), you gain deep insight into every third-party and open-source component. Platforms like BlueVoyant’s Supply Chain Defense now automatically solicit SBOMs from vendors, parse component lists, and cross-reference live vulnerability databases arXiv+6BlueVoyant+6BlueVoyant+6.

Continuous SBOM analysis allows you to:

  • Detect newly disclosed vulnerabilities (including zero-days) in embedded components

  • Enforce patch policies by alerting downstream, dependent teams

  • Document compliance with SBOM mandates like EO 14028, NIS2, DORAriskrecon.com+8BlueVoyant+8Panorays+8AuditBoard

Academic studies highlight both the power and challenges of SBOMs: they dramatically improve visibility and risk prioritization, though accuracy depends on tooling and trust mechanisms BlueVoyant+3arXiv+3arXiv+3.

By integrating SBOM scanning into CI/CD pipelines and TPRM platforms, you gain near-instant risk metrics tied to vendor releases—no manual sharing or delays.

B. Telemetry & Vendor Hygiene Ratings

SBOM gives you what’s there—telemetry tells you what’s happening. Vendors exhibit patterns: patching behavior, certificate rotation, service uptime, internet configuration. SecurityScorecard, Bitsight, and RiskRecon continuously track hundreds of external signals—open ports, cert lifecycles, leaked credentials, dark-web activity—to generate objective hygiene scores arXiv+7Bitsight+7BlueVoyant+7.

By feeding these scores into your TPRM workflow, you can:

  • Rank vendors by real-time risk posture

  • Trigger assessments or alerts when hygiene drops beyond set thresholds

  • Compare cohorts of vendors to prioritize remediation

Third-party risk intelligence isn’t a luxury—it’s a necessity. As CyberSaint’s blog explains: “True TPRI gives you dynamic, contextualized insight into which third parties matter most, why they’re risky, and how that risk evolves”BlueVoyant+3cybersaint.io+3AuditBoard+3.

C. Contract & SLA Enforcement: Automated Triggers

Contracts and SLAs are the foundation—but obsolete if not digitally enforced. What if your systems could trigger compliance actions automatically?

  • Contract clauses tied to SBOM disclosure frequency, patch cycles, or signal scores

  • Automated notices when vendor security ratings dip or new vulnerabilities appear

  • Escalation workflows for missing SBOMs, low hygiene ratings, or SLA breaches

Venminder and ProcessUnity offer SLA management modules that integrate risk signals and automate vendor notifications Reflectiz+1Bitsight+1. By codifying SLA-negotiated penalties (e.g., credits, remediation timelines) you gain leverage—backed by data, not inference.

For maximum effect, integrate enforcement into GRC platforms: low scores trigger risk team involvement, legal drafts automatic reminders, remediation status migrates into the vendor dossier.

D. Dashboarding & Alerts: Risk Thresholds

Data is meaningless unless visualized and actioned. Create dashboards that blend:

  • SBOM vulnerability counts by vendor/product

  • Vendor hygiene ratings, benchmarks, changes over time

  • Contract compliance indicators: SBOM delivered on time? SLAs met?

  • Incident and breach telemetry

Thresholds define risk states. Alerts trigger when:

  • New CVEs appear in vendor code

  • Hygiene scores fall sharply

  • Contracts are breached

Platforms like Mitratech and SecurityScorecard centralize these signals into unified risk registers—complete with automated playbooks SecurityScorecardMitratech. This transforms raw alerts into structured workflows.

Dashboards should display:

  • Risk heatmaps by vendor tier

  • Active incidents and required follow-ups

  • Age of SBOMs, patch status, and SLAs by vendor

Visual indicators let risk owners triage immediately—before an alert turns into a breach.

Implementation: Build the Dialogue

How do you go from theory to practice? It starts with collaboration—and automation.

Tool Setup

Begin by integrating SBOM ingestion and vulnerability scanning into your TPRM toolchain. Work with vendors to include SBOMs in release pipelines. Next, onboard security-rating providers—SecurityScorecard, Bitsight, etc.—via APIs. Map contract clauses to data feeds: SBOM frequency, patch turnaround, rating thresholds.

Finally, build workflows:

  • Data ingestion: SBOMs, telemetry scores, breach signals

  • Risk correlation: combine signals per vendor

  • Automated triage: alerts route to risk teams when threshold is breached

  • Enforcement: contract notifications, vendor outreach, escalations

Alert Triage Flows

A vendor’s hygiene score drops by 20%? Here’s the flow:

  1. Automated alert flags vendor; dashboard marks “at-risk.”

  2. Risk team reviews dashboard, finds increase in certificate expiry and open ports.

  3. Triage call with Vendor Ops; request remediation plan with 48-hour resolution SLA.

  4. Log call and remediation deadline in GRC.

  5. If unresolved by SLA cutoff, escalate to legal and trigger contract clause (e.g., discount, audit provisioning).

For vulnerabilities in SBOM components:

  1. New CVE appears in vendor’s latest SBOM.

  2. Automated notification to vendor, requesting patch timeline.

  3. Pass SBOM and remediation deadline into tracking system.

  4. Once patch is delivered, scan again and confirm resolution.

By automating as much of this as possible, you dramatically shorten mean time to response—and remove manual bottlenecks.

Breach Coordination Playbooks

If a vendor breach occurs:

  1. Risk platform alerts detection (e.g., breach flagged by telemetry provider).

  2. Initiate incident coordination: vendor-led investigation, containment, ATO review.

  3. Use standard playbooks: vendor notification, internal stakeholder actions, regulatory reporting triggers.

  4. Continually update incident dashboard; sunset workflow after resolution and post-mortem.

This coordination layer ensures your response is structured and auditable—and leverages continuous signals for early detection.

Organizational Dialogue

Success requires cross-functional communication:

  • Procurement must include SLA clauses and SBOM requirements

  • DevSecOps must connect build pipelines and SBOM generation

  • Legal must codify enforcement actions

  • Security ops must monitor alerts and lead triage

  • Vendors must deliver SBOMs, respond to issues, and align with patch SLAs

Continuous risk pipelines thrive when everyone knows their role—and tools reflect it.

Examples & Use Cases

Illustrative Story: A SaaS vendor pushes out a feature update. Their new SBOM reveals a critical library with an unfixed CVE. Automatically, your TPRM pipeline flags the issue, notifies the vendor, and begins SLA-tracked remediation. Within hours, a patch is released, scanned, and approved—preventing a potential breach. That same vendor’s weak TLS config had dropped their security rating; triage triggered remediation before attackers could exploit. With continuous signals and automation baked into the fabric of your TPRM process, you shift from reactive firefighting to proactive defense.

Conclusion

Static audits and old-school vendor scoring simply won’t cut it anymore. Breaches like SolarWinds and MOVEit expose the fractures in point-in-time controls. To protect enterprise ecosystems today, organizations need pipelines that continuously intake SBOMs, telemetry, contract compliance, and breach data—while automating triage, enforcement, and incident orchestration.

The path isn’t easy, but it’s clear: implement SBOM scanning, integrate hygiene telemetry, codify enforcement via SLAs, and visualize risk in real time. When culture, technology, and contracts are aligned, what was once a blind spot becomes a hardened perimeter. In supply chain defense, constant vigilance isn’t optional—it’s mandatory.

More Info, Help, and Questions

MicroSolved is standing by to discuss vendor risk management, automation of security processes, and bleeding-edge security solutions with your team. Simply give us a call at +1.614.351.1237 or drop us a line at info@microsolved.com to leverage our 32+ years of experience for your benefit. 

The Zero Trust Scorecard: Tracking Culture, Compliance & KPIs

Posted on by
Reply

The Plateau: A CISO’s Zero Trust Dilemma

I met with a CISO last month who was stuck halfway up the Zero Trust mountain. Their team had invested in microsegmentation, MFA was everywhere, and cloud entitlements were tightened to the bone. Yet, adoption was stalling. Phishing clicks still happened. Developers were bypassing controls to “get things done.” And the board wanted proof their multi-million-dollar program was working.

This is the Zero Trust Plateau. Many organizations hit it. Deploying technologies is only the first leg of the journey. Sustaining Zero Trust requires cultural change, ongoing measurement, and the ability to course-correct quickly. Otherwise, you end up with a static architecture instead of a dynamic security posture.

This is where the Zero Trust Scorecard comes in.

ZeroTrustScorecard

Why Metrics Change the Game

Zero Trust isn’t a product. It’s a philosophy—and like any philosophy, its success depends on how people internalize and practice it over time. The challenge is that most organizations treat Zero Trust as a deployment project, not a continuous process.

Here’s what usually happens:

  • Post-deployment neglect – Once tools are live, metrics vanish. Nobody tracks if users adopt new patterns or if controls are working as intended.

  • Cultural resistance – Teams find workarounds. Admins disable controls in dev environments. Business units complain that “security is slowing us down.”

  • Invisible drift – Cloud configurations mutate. Entitlements creep back in. Suddenly, your Zero Trust posture isn’t so zero anymore.

This isn’t about buying more dashboards. It’s about designing a feedback loop that measures technical effectiveness, cultural adoption, and compliance drift—so you can see where to tune and improve. That’s the promise of the Scorecard.

The Zero Trust Scorecard Framework

A good Zero Trust Scorecard balances three domains:

  1. Cultural KPIs

  2. Technical KPIs

  3. Compliance KPIs

Let’s break them down.

🧠 Cultural KPIs: Measuring Adoption and Resistance

  • Stakeholder Adoption Rates
    Track how quickly and completely different business units adopt Zero Trust practices. For example:

    • % of developers using secure APIs instead of legacy connections.

    • % of employees logging in via SSO/MFA.

  • Training Completion & Engagement
    Zero Trust requires a mindset shift. Measure:

    • Security training completion rates (mandatory and voluntary).

    • Behavioral change: number of reported phishing emails per user.

  • Phishing Resistance
    Run regular phishing simulations. Watch for:

    • % of users clicking on simulated phishing emails.

    • Time to report suspicious messages.

Culture is the leading indicator. If people aren’t on board, your tech KPIs won’t matter for long.

⚙️ Technical KPIs: Verifying Your Architecture Works

  • Authentication Success Rates
    Monitor login success/failure patterns:

    • Are MFA denials increasing because of misconfiguration?

    • Are users attempting legacy protocols (e.g., NTLM, basic auth)?

  • Lateral Movement Detection
    Test whether microsegmentation and identity controls block lateral movement:

    • % of simulated attacker movement attempts blocked.

    • Number of policy violations detected in network flows.

  • Device Posture Compliance
    Check device health before granting access:

    • % of devices meeting patching and configuration baselines.

    • Remediation times for out-of-compliance devices.

These KPIs help answer: “Are our controls operating as designed?”

📜 Compliance KPIs: Staying Aligned and Audit-Ready

  • Audit Pass Rates
    Track the % of internal and external audits passed without exceptions.

  • Cloud Posture Drift
    Use tools like CSPM (Cloud Security Posture Management) to measure:

    • Number of critical misconfigurations over time.

    • Mean time to remediate drift.

  • Policy Exception Requests
    Monitor requests for policy exceptions. A high rate could signal usability issues or cultural resistance.

Compliance metrics keep regulators and leadership confident that Zero Trust isn’t just a slogan.

Building Your Zero Trust Scorecard

So how do you actually build and operationalize this?

🎯 1. Define Goals and Data Sources

Start with clear objectives for each domain:

  • Cultural: “Reduce phishing click rate by 50% in 6 months.”

  • Technical: “Block 90% of lateral movement attempts in purple team exercises.”

  • Compliance: “Achieve zero critical cloud misconfigurations within 90 days.”

Identify data sources: SIEM, identity providers (Okta, Azure AD), endpoint managers (Intune, JAMF), and security awareness platforms.

📊 2. Set Up Dashboards with Examples

Create dashboards that are consumable by non-technical audiences:

  • For executives: High-level trends—“Are we moving in the right direction?”

  • For security teams: Granular data—failed authentications, policy violations, device compliance.

Example Dashboard Widgets:

  • % of devices compliant with Zero Trust posture.

  • Phishing click rates by department.

  • Audit exceptions over time.

Visuals matter. Use red/yellow/green indicators to show where attention is needed.

📅 3. Establish Cadence and Communication

A Scorecard is useless if nobody sees it. Embed it into your organizational rhythm:

  • Weekly: Security team reviews technical KPIs.

  • Monthly: Present Scorecard to business unit leads.

  • Quarterly: Share executive summary with the board.

Use these touchpoints to celebrate wins, address resistance, and prioritize remediation.

Why It Works

Zero Trust isn’t static. Threats evolve, and so do people. The Scorecard gives you a living view of your Zero Trust program—cultural, technical, and compliance health in one place.

It keeps you from becoming the CISO stuck halfway up the mountain.

Because in Zero Trust, there’s no summit. Only the climb.

Questions and Getting Help

Want to discuss ways to progress and overcome the plateau? Need help with planning, building, managing, or monitoring Zero Trust environments? 

Just reach out to MicroSolved for a no-hassle, no-pressure discussion of your needs and our capabilities. 

Phone: +1.614.351.1237 or Email: info@microsolved.com

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

How to Secure Your SOC’s AI Agents: A Practical Guide to Orchestration and Trust

Posted on by
Reply

Automation Gone Awry: Can We Trust Our AI Agents?

Picture this: it’s 2 AM, and your SOC’s AI triage agent confidently flags a critical vulnerability in your core application stack. It even auto-generates a remediation script to patch the issue. The team—running lean during the night shift—trusts the agent’s output and pushes the change. Moments later, key services go dark. Customers start calling. Revenue grinds to a halt.

AITeamMember

This isn’t science fiction. We’ve seen AI agents in SOCs produce flawed methodologies, hallucinate mitigation steps, or run outdated tools. Bad scripts, incomplete fixes, and overly confident recommendations can create as much risk as the threats they’re meant to contain.

As SOCs lean harder on agentic AI for triage, enrichment, and automation, we face a pressing question: how much trust should we place in these systems, and how do we secure them before they secure us?

Why This Matters Now

SOCs are caught in a perfect storm: rising attack volumes, an acute cybersecurity talent shortage, and ever-tightening budgets. Enter AI agents—promising to scale triage, correlate threat data, enrich findings, and even generate mitigation scripts at machine speed. It’s no wonder so many SOCs are leaning into agentic AI to do more with less.

But there’s a catch. These systems are far from infallible. We’ve already seen agents hallucinate mitigation steps, recommend outdated tools, or produce complex scripts that completely miss the mark. The biggest risk isn’t the AI itself—it’s the temptation to treat its advice as gospel. Too often, overburdened analysts assume “the machine knows best” and push changes without proper validation.

To be clear, AI agents are remarkably capable—far more so than many realize. But even as they grow more autonomous, human vigilance remains critical. The question is: how do we structure our SOCs to safely orchestrate these agents without letting efficiency undermine security?

Securing AI-SOC Orchestration: A Practical Framework

1. Trust Boundaries: Start Low, Build Slowly

Treat your SOC’s AI agents like junior analysts—or interns on their first day. Just because they’re fast and confident doesn’t mean they’re trustworthy. Start with low privileges and limited autonomy, then expand access only as they demonstrate reliability under supervision.

Establish a graduated trust model:

  • New AI use cases should default to read-only or recommendation mode.

  • Require human validation for all changes affecting production systems or critical workflows.

  • Slowly introduce automation only for tasks that are well-understood, extensively tested, and easily reversible.

This isn’t about mistrusting AI—it’s about understanding its limits. Even the most advanced agent can hallucinate or misinterpret context. SOC leaders must create clear orchestration policies defining where automation ends and human oversight begins.

2. Failure Modes: Expect Mistakes, Contain the Blast Radius

AI agents in SOCs can—and will—fail. The question isn’t if, but how badly. Among the most common failure modes:

  • Incorrect or incomplete automation that doesn’t fully mitigate the issue.

  • Buggy or broken code generated by the AI, particularly in complex scripts.

  • Overconfidence in recommendations due to lack of QA or testing pipelines.

To mitigate these risks, design your AI workflows with failure in mind:

  • Sandbox all AI-generated actions before they touch production.

  • Build in human QA gates, where analysts review and approve code, configurations, or remediation steps.

  • Employ ensemble validation, where multiple AI agents (or models) cross-check each other’s outputs to assess trustworthiness and completeness.

  • Adopt the mindset of “assume the AI is wrong until proven otherwise” and enforce risk management controls accordingly.

Fail-safe orchestration isn’t about stopping mistakes—it’s about limiting their scope and catching them before they cause damage.

3. Governance & Monitoring: Watch the Watchers

Securing your SOC’s AI isn’t just about technical controls—it’s about governance. To orchestrate AI agents safely, you need robust oversight mechanisms that hold them accountable:

  • Audit Trails: Log every AI action, decision, and recommendation. If an agent produces bad advice or buggy code, you need the ability to trace it back, understand why it failed, and refine future prompts or models.

  • Escalation Policies: Define clear thresholds for when AI can act autonomously and when it must escalate to a human analyst. Critical applications and high-risk workflows should always require manual intervention.

  • Continuous Monitoring: Use observability tools to monitor AI pipelines in real time. Treat AI agents as living systems—they need to be tuned, updated, and occasionally reined in as they interact with evolving environments.

Governance ensures your AI doesn’t just work—it works within the parameters your SOC defines. In the end, oversight isn’t optional. It’s the foundation of trust.

Harden Your AI-SOC Today: An Implementation Guide

Ready to secure your AI agents? Start here.

✅ Workflow Risk Assessment Checklist

  • Inventory all current AI use cases and map their access levels.

  • Identify workflows where automation touches production systems—flag these as high risk.

  • Review permissions and enforce least privilege for every agent.

✅ Observability Tools for AI Pipelines

  • Deploy monitoring systems that track AI inputs, outputs, and decision paths in real time.

  • Set up alerts for anomalies, such as sudden shifts in recommendations or output patterns.

✅ Tabletop AI-Failure Simulations

  • Run tabletop exercises simulating AI hallucinations, buggy code deployments, and prompt injection attacks.

  • Carefully inspect all AI inputs and outputs during these drills—look for edge cases and unexpected behaviors.

  • Involve your entire SOC team to stress-test oversight processes and escalation paths.

✅ Build a Trust Ladder

  • Treat AI agents as interns: start them with zero trust, then grant privileges only as they prove themselves through validation and rigorous QA.

  • Beware the sunk cost fallacy. If an agent consistently fails to deliver safe, reliable outcomes, pull the plug. It’s better to lose automation than compromise your environment.

Securing your AI isn’t about slowing down innovation—it’s about building the foundations to scale safely.

Failures and Fixes: Lessons from the Field

Failures

  • Naïve Legacy Protocol Removal: An AI-based remediation agent identifies insecure Telnet usage and “remediates” it by deleting the Telnet reference but ignores dependencies across the codebase—breaking upstream systems and halting deployments.

  • Buggy AI-Generated Scripts: A code-assist AI generates remediation code for a complex vulnerability. When executed untested, the script crashes services and exposes insecure configurations.

Successes

  • Rapid Investigation Acceleration: One enterprise SOC introduced agentic workflows that automated repetitive tasks like data gathering and correlation. Investigations that once took 30 minutes now complete in under 5 minutes, with increased analyst confidence.

  • Intelligent Response at Scale: A global security team deployed AI-assisted systems that provided high-quality recommendations and significantly reduced time-to-response during active incidents.

Final Thoughts: Orchestrate With Caution, Scale With Confidence

AI agents are here to stay, and their potential in SOCs is undeniable. But trust in these systems isn’t a given—it’s earned. With careful orchestration, robust governance, and relentless vigilance, you can build an AI-enabled SOC that augments your team without introducing new risks.

In the end, securing your AI agents isn’t about holding them back. It’s about giving them the guardrails they need to scale your defenses safely.

For more info and help, contact MicroSolved, Inc. 

We’ve been working with SOCs and automation for several years, including AI solutions. Call +1.614.351.1237 or send us a message at info@microsolved.com for a stress-free discussion of our capabilities and your needs. 

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.