Methodology: MailItemsAccessed-Based Investigation for BEC in Microsoft 365

Posted on November 3, 2025 by Brent Huston

When your organization faces a business-email compromise (BEC) incident, one of the hardest questions is: “What did the attacker actually read or export?” Conventional logs often show only sign-ins or outbound sends, but not the depth of mailbox item access. The MailItemsAccessed audit event in Microsoft 365 Unified Audit Log (UAL) brings far more visibility — if configured correctly. This article outlines a repeatable, defensible process for investigation using that event, from readiness verification to scoping and reporting.

Objective

Provide a repeatable, defensible process to identify, scope, and validate email exposure in BEC investigations using the MailItemsAccessed audit event.

Phase 1 — Readiness Verification (Pre-Incident)

Before an incident hits, you must validate your logging and audit posture. These steps ensure you’ll have usable data.

1. Confirm Licensing

Verify your tenant’s audit plan under Microsoft Purview Audit (Standard or Premium).
- Audit (Standard): default retention 180 days (previously 90).
- Audit (Premium): longer retention (e.g., 365 days or more), enriched logs.
Confirm that your license level supports the MailItemsAccessed event. Many sources state this requires Audit Premium or an E5-level compliance add-on.

2. Validate Coverage

Confirm mailbox auditing is on by default for user mailboxes. Microsoft states this for Exchange Online.
Confirm that MailItemsAccessed is part of the default audit set (or if custom audit sets exist, that it’s included). According to Microsoft documentation: the MailItemsAccessed action “covers all mail protocols … and is enabled by default for users assigned an Office 365 E3/E5 or Microsoft 365 E3/E5 licence.”
For tenants with customised audit sets, ensure the Microsoft defaults are re-applied so that MailItemsAccessedisn’t inadvertently removed.

3. Retention & Baseline

Record what your current audit-log retention policy is (e.g., 180 days vs 365 days) so you know how far back you can search.
Establish a baseline volume of MailItemsAccessed events—how many are generated from normal activity. That helps define thresholds for abnormal behaviour during investigation.

Phase 2 — Investigation Workflow (During Incident)

Once an incident is underway and you have suspected mailboxes, follow structured investigation steps.

1. Identify Affected Accounts

From your alarm sources (e.g., anomalous sign-in alerts, inbound or outbound rule creation, unusual inbox rules, compromised credentials) compile a list of mailboxes that might have been accessed.

2. Extract Evidence

In the Purview portal → Audit → filter for Activity = MailItemsAccessed, specifying the time range that covers suspected attacker dwell time.
Export the results to CSV via the Unified Audit Log.

3. Correlate Access Sessions

Group the MailItemsAccessed results by key session indicators:

ClientIP
SessionId
UserAgent / ClientInfoString

Flag sessions that show:

Unknown or non-corporate IP addresses (e.g., external ASN)
Legacy protocols (IMAP, POP, ActiveSync) or bulk-sync behaviour
User agents indicating automated tooling or scripting

4. Quantify Exposure

Count distinct ItemIds and FolderPaths to determine how many items and which folders were accessed.
Look for throttling indicators (for example more than ~1,000 MailItemsAccessed events in 24 h for a single user may indicate scripted or bulk access).
Use the example KQL queries below (see Section “KQL Example Snippets”).

5. Cross-Correlate with Other Events

Overlay these results with Send audit events and InboxRule/New-InboxRule events to detect lateral-phish, rule-based fraud or data-staging behaviour.
For example, access events followed by mass sends indicate attacker may have read and then exfiltrated or used the account for fraud.

6. Validate Exfil Path

Check the client protocol used by the session. If the client is REST API, bulk sync or legacy protocol, that may indicate the attacker is exfiltrating rather than simply reading.
If MailItemsAccessed shows items accessed using a legacy IMAP/POP or ActiveSync session — that is a red flag for mass download.

Phase 3 — Analysis & Scoping

Once raw data is collected, move into analysis to scope the incident.

1. Establish Attack Session Timeline

Combine sign-in logs (from Microsoft Entra ID Sign‑in Logs) with MailItemsAccessed events to reconstruct dwell time and sequence.
Determine when attacker first gained access, how long they stayed, and when they left.

2. Define Affected Items

Deliver an itemised summary (folder path, count of items, timestamps) of mailbox items accessed.
Limit exposure claims to the items you have logged evidence for — do not assume access of the entire mailbox unless logs show it (or you have other forensic evidence).

3. Corroborate with Throttling and Send Events

If you see unusual high-volume access plus spike in Send events or inbox rule changes, you can conclude automated or bulk access occurred.
Document IOCs (client IPs, session IDs, user-agent strings) tied to the malicious session.

Phase 4 — Reporting & Validation

After investigation you report findings and validate control-gaps.

1. Evidence Summary

Your report should document:

Tenant license type and retention (Audit Standard vs Premium)
Audit coverage verification (mailbox auditing enabled, MailItemsAccessed present)
Affected item count, folder paths, session data (IPs, protocol, timeframe)
Indicators of compromise (IOCs) and signs of mass or scripted access

2. Limitations

Be transparent about limitations:

Upgrading to Audit Premium mid-incident will not backfill missing MailItemsAccessed data for the earlier period. Sources note this gap.
If mailbox auditing or default audit-sets were customised (and MailItemsAccessed omitted), you may lack full visibility. Example commentary notes this risk.

3. Recommendations

Maintain Audit Premium licensing for at-risk tenants (e.g., high-value executive mailboxes or those handling sensitive data).
Pre-stage KQL dashboards to detect anomalies (e.g., bursts of MailItemsAccessed, high counts per hour or per day) so you don’t rely solely on ad-hoc searches.
Include audit-configuration verification (licensing, mail-audit audit-set, retention) in your regular vCISO or governance audit cadence.

KQL Example Snippets

MITRE ATT&CK Mapping

Tactic	Technique	ID
Collection	Email Collection	T1114.002
Exfiltration	Exfiltration Over Web Services	T1567.002
Discovery	Cloud Service Discovery	T1087.004
Defense Evasion	Valid Accounts (Cloud)	T1078.004

These mappings illustrate how MailItemsAccessed visibility ties directly into attacker-behaviour frameworks in cloud email contexts.

Minimal Control Checklist

Verify Purview Audit plan and retention
Validate MailItemsAccessed events present/searchable for a sample of users
Ensure mailbox auditing defaults (default audit-set) restored and active
Pre-stage anomaly detection queries / dashboards for mailbox-access bursts

Conclusion

When investigating a BEC incident, possession of high-fidelity audit data like MailItemsAccessed transforms your investigation from guesswork into evidence-driven clarity. The key is readiness: licence appropriately, validate your coverage, establish baselines, and when a breach occurs follow a structured workflow from extraction to scoping to reporting. Without that groundwork your post-incident forensics may hit blind spots. But with it you increase your odds of confidently quantifying exposure, attributing access and closing the loop.

Prepare, detect, dissect—repeatably.

References

Microsoft Learn: Manage mailbox auditing – “Mailbox audit logging is turned on by default in all organizations.”
Microsoft Learn: Use MailItemsAccessed to investigate compromised accounts – “The MailItemsAccessed action … is enabled by default for users that are assigned an Office 365 E3/E5 or Microsoft 365 E3/E5 license.”
Microsoft Learn: Auditing solutions in Microsoft Purview – licensing and search prerequisites.
Office365ITPros: Enable MailItemsAccessed event for Exchange Online – “Purview Audit Premium is included in Office 365 E5 and … Audit (Standard) is available to E3 customers.”
TrustedSec blog: MailItemsAccessed woes – “According to Microsoft, this event is only accessible if you have the Microsoft Purview Audit (Premium) functionality.”
Practical365: Microsoft’s slow delivery of MailItemsAccessed audit event – retention commentary.
O365Info: Manage audit log retention policies – up to 10 years for Premium.
Office365ITPros: Mailbox audit event ingestion issues for E3 users.
RedCanary blog: Entra ID service principals and BEC – “MailItemsAccessed is a very high volume record …”

* 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.

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

Posted on October 27, 2025 by Brent Huston

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

Assess your maturity and readiness
Define use‑cases with business value
Build foundation: data, tooling, skills
Pilot, iterate, scale
Embed human‑machine teaming and continuous improvement
Maintain governance, ethics and risk oversight
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

ISC2 AI Adoption Pulse Survey 2025
IBM X-Force Threat Intelligence Index 2025
Accenture State of Cybersecurity Resilience 2025
Cisco 2025 Cybersecurity Readiness Index
Darktrace State of AI Cybersecurity Report 2025
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 October 20, 2025 by Brent Huston

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.

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 October 14, 2025 by Brent Huston

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:

Awareness & Inventory
- Catalog AI/ML assets
- Basic training & policies
- Executive buy-in
Baseline Controls
- Access controls, audit logging
- Data sanitization & DLP for AI pipelines
- Shadow AI monitoring
Model Protection & Hardening
- Differential privacy, adversarial testing, prompt filters
- Runtime anomaly detection
- Sandbox staging
Audit, Metrics & Continuous Improvement
- Regular red teaming
- Drift detection & revalidation
- Integration into ERM / compliance
- Internal assurance & audit loops
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.

Cut SOC Noise with an Alert-Quality SLO: A Practical Playbook for Security Teams

Posted on September 29, 2025 by Brent Huston

Security teams don’t burn out because of “too many threats.” They burn out because of too much junk between them and the real threats: noisy detections, vague alerts, fragile rules, and AI that promises magic but ships mayhem.

SOC

Here’s a simple fix that works in the real world: treat alert quality like a reliability objective. Put noise on a hard budget and enforce a ship/rollback gate—exactly like SRE error budgets. We call it an Alert-Quality SLO (AQ-SLO) and it can reclaim 20–40% of analyst time for higher-value work like hunts, tuning, and purple-team exercises.

The Core Idea: Put a Budget on Junk

Alert-Quality SLO (AQ-SLO): set an explicit ceiling for non-actionable alerts per analyst-hour (NAAH). If a new rule/model/AI feed pushes you over budget, it doesn’t ship—or it auto-rolls back.

Think “error budgets,” but applied to SOC signal quality.

Working definitions (plain language)

Non-actionable alert: After triage, it requires no ticket, containment, or tuning request—just closes.
Analyst-hour: One hour of human triage time (any level).
AQ-SLO: Maximum tolerated non-actionables per analyst-hour over a rolling window.

Baselines and Targets (Start Here)

Before you tune, measure. Collect 2–4 weeks of baselines:

Non-actionable rate (NAR) = (Non-actionables / Total alerts) × 100
Non-actionables per analyst-hour (NAAH) = Non-actionables / Analyst-hours
Mean time to triage (MTTT) = Average minutes to disposition (track P90, too)

Initial SLO targets (adjust to your environment):

NAAH ≤ 5.0 (Gold ≤ 3.0, Silver ≤ 5.0, Bronze ≤ 7.0)
NAR ≤ 35% (Gold ≤ 20%, Silver ≤ 35%, Bronze ≤ 45%)
MTTT ≤ 6 min (with P90 ≤ 12 min)

These numbers are intentionally pragmatic: tight enough to curb fatigue, loose enough to avoid false heroics.

Ship/Rollback Gate for Rules & AI

Every new detector—rule, correlation, enrichment, or AI model—must prove itself in shadow mode before it’s allowed to page humans.

Shadow-mode acceptance (7 days recommended):

NAAH ≤ 3.0, or
≥ 30% precision uplift vs. control, and
No regression in P90 MTTT or paging load

Enforcement: If the detector breaches the budget 3 days in 7, auto-disable or revert and capture a short post-mortem. You’re not punishing innovation—you’re defending analyst attention.

Minimum Viable Telemetry (Keep It Simple)

For every alert, capture:

detector_id
created_at
triage_outcome → {actionable | non_actionable}
triage_minutes
root_cause_tag → {tuning_needed, duplicate, asset_misclass, enrichment_gap, model_hallucination, rule_overlap}

Hourly roll-ups to your dashboard:

NAAH, NAR, MTTT (avg & P90)
Top 10 noisiest detectors by non-actionable volume and triage cost

This is enough to run the whole AQ-SLO loop without building a data lake first.

Operating Rhythm (SOC-wide, 45 Minutes/Week)

Noise Review (20 min): Examine the Top 10 noisiest detectors → keep, fix, or kill.
Tuning Queue (15 min): Assign PRs/changes for the 3 biggest contributors; set owners and due dates.
Retro (10 min): Are we inside the budget? If not, apply the rollback rule. No exceptions.

Make it boring, repeatable, and visible. Tie it to team KPIs and vendor SLAs.

What to Measure per Detector/Model

Precision @ triage = actionable / total
NAAH contribution = non-actionables from this detector / analyst-hours
Triage cost = Σ triage_minutes
Kill-switch score = weighted blend of (precision↓, NAAH↑, triage cost↑)

Rank detectors by kill-switch score to drive your weekly agenda.

Formulas You Can Drop into a Sheet

NAAH = NON_ACTIONABLE_COUNT / ANALYST_HOURS

NAR% = (NON_ACTIONABLE_COUNT / TOTAL_ALERTS) * 100

MTTT = AVERAGE(TRIAGE_MINUTES)

MTTT_P90 = PERCENTILE(TRIAGE_MINUTES, 0.9)

ERROR_BUDGET_USED = max(0, (NAAH – SLO_NAAH) / SLO_NAAH)

These translate cleanly into Grafana, Kibana/ELK, BigQuery, or a simple spreadsheet.

Fast Implementation Plan (14 Days)

Day 1–3: Instrument triage outcomes and minutes in your case system. Add the root-cause tags above.

Day 4–10: Run all changes in shadow mode. Publish hourly NAAH/NAR/MTTT to a single dashboard.

Day 11: Freeze SLOs (start with ≤ 5 NAAH, ≤ 35% NAR).

Day 12–14: Turn on auto-rollback for any detector breaching budget.

If your platform supports feature flags, wrap detectors with a kill-switch. If not, document a manual rollback path and make it muscle memory.

SOC-Wide Incentives (Make It Stick)

Team KPI: % of days inside AQ-SLO (target ≥ 90%).
Engineering KPI: Time-to-fix for top noisy detectors (target ≤ 5 business days).
Vendor/Model SLA: Noise clauses—breach of AQ-SLO triggers fee credits or disablement.

This aligns incentives across analysts, engineers, and vendors—and keeps the pager honest.

Why AQ-SLOs Work (In Practice)

Cuts alert fatigue and stabilizes on-call burdens.
Reclaims 20–40% analyst time for hunts, purple-team work, and real incident response.
Turns AI from hype to reliability: shadow-mode proof + rollback by budget makes “AI in the SOC” shippable.
Improves organizational trust: leadership gets clear, comparable metrics for signal quality and human cost.

Common Pitfalls (and How to Avoid Them)

Chasing zero noise. You’ll starve detection coverage. Use realistic SLOs and iterate.
No root-cause tags. You can’t fix what you can’t name. Keep the tag set small and enforced.
Permissive shadow-mode. If it never ends, it’s not a gate. Time-box it and require uplift.
Skipping rollbacks. If you won’t revert noisy changes, your SLO is a wish, not a control.
Dashboard sprawl. One panel with NAAH, NAR, MTTT, and the Top 10 noisiest detectors is enough.

Policy Addendum (Drop-In Language You Can Adopt Today)

Alert-Quality SLO: The SOC shall maintain non-actionable alerts ≤ 5 per analyst-hour on a 14-day rolling window. New detectors (rules, models, enrichments) must pass a 7-day shadow-mode trial demonstrating NAAH ≤ 3 or ≥ 30% precision uplift with no P90 MTTT regressions. Detectors that breach the SLO on 3 of 7 days shall be disabled or rolled back pending tuning. Weekly noise-review and tuning queues are mandatory, with owners and due dates tracked in the case system.

Tune the numbers to fit your scale and risk tolerance, but keep the mechanics intact.

What This Looks Like in the SOC

An engineer proposes a new AI phishing detector.
It runs in shadow mode for 7 days, with precision measured at triage and NAAH tracked hourly.
It shows a 36% precision uplift vs. the current phishing rule set and no MTTT regression.
It ships behind a feature flag tied to the AQ-SLO budget.
Three days later, a vendor feed change spikes duplicate alerts. The budget breaches.
The feature flag kills the noisy path automatically, a ticket captures the post-mortem, and the tuning PR lands in 48 hours.
Analyst pager load stays stable; hunts continue on schedule.

That’s what operationalized AI looks like when noise is a first-class reliability concern.

Want Help Standing This Up?

MicroSolved has implemented AQ-SLOs and ship/rollback gates in SOCs of all sizes—from credit unions to automotive suppliers—across SIEMs, EDR/XDR, and AI-assisted detection stacks. We can help you:

Baseline your current noise profile (NAAH/NAR/MTTT)
Design your shadow-mode trials and acceptance gates
Build the dashboard and auto-rollback workflow
Align SLAs, KPIs, and vendor contracts to AQ-SLOs
Train your team to run the weekly operating rhythm

Get in touch: Visit microsolved.com/contact or email info@microsolved.com to talk with our team about piloting AQ-SLOs in your environment.

* 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.

Machine Identity Management: The Overlooked Cyber Risk and What to Do About It

Posted on September 15, 2025 by Brent Huston

The term “identity” in cybersecurity usually summons images of human users: employees, contractors, customers signing in, multi‑factor authentication, password resets. But lurking behind the scenes is another, rapidly expanding domain of identities: non‑human, machine identities. These are the digital credentials, certificates, service accounts, keys, tokens, device identities, secrets, etc., that allow machines, services, devices, and software to authenticate, communicate, and operate securely.

CyberLaptop

Machine identities are often under‑covered, under‑audited—and yet they constitute a growing, sometimes catastrophic attack surface. This post defines what we mean by machine identity, explores why it is risky, surveys real incidents, lays out best practices, tools, and processes, and suggests metrics and a roadmap to help organizations secure their non‑human identities at scale.

What Are Machine Identities

Broadly, a machine identity is any credential, certificate, or secret that a non‑human entity uses to prove its identity and communicate securely. Key components include:

Digital certificates and Public Key Infrastructure (PKI)
Cryptographic keys
Secrets, tokens, and API keys
Device and workload identities

These identities are used in many roles: securing service‑to‑service communications, granting access to back‑end databases, code signing, device authentication, machine users (e.g. automated scripts), etc.

Why Machine Identities Are Risky

Here are major risk vectors around machine identities:

Proliferation & Sprawl
Shadow Credentials / Poor Visibility
Lifecycle Mismanagement
Misuse or Overprivilege
Credential Theft / Compromise
Operational & Business Risks

Real Incidents and Misuse

Incident	What happened	Root cause / machine identity failure	Impact
Microsoft Teams Outage (Feb 2020)	Microsoft users unable to sign in / use Teams/Office services	An authentication certificate expired.	Several-hour outage for many users; disruption of business communication and collaboration.
Microsoft SharePoint / Outlook / Teams Certificate Outage (2023)	SharePoint / Teams / Outlook service problems	Mis‑assignment / misuse of TLS certificate or other certificate mis‑configuration.	Users experienced interruption; even if the downtime was short, it affected trust and operations.
NVIDIA / LAPSUS$ breach	Code signing certificates stolen in breach	Attackers gained access to private code signing certificates; used them to sign malware.	Malware signed with legitimate certificates; potential for large-scale spread, supply chain trust damage.
GitHub (Dec 2022)	Attack on “machine account” / repositories; code signing certificates stolen or exposed	A compromised personal access token associated with a machine account allowed theft of code signing certificates.	Risk of malicious software, supply chain breach.

Best Practices for Securing Machine Identities

Establish Full Inventory & Ownership
Adopt Lifecycle Management
Least Privilege & Segmentation
Use Secure Vaults / Secret Management Systems
Automation and Policy Enforcement
Monitoring, Auditing, Alerting
Incident Recovery and Revocation Pathways
Integrate with CI/CD / DevOps Pipelines

Tools & Vendor vs In‑House

Requirement	Key Features to Look For	Vendor Solutions	In-House Considerations
Discovery & Inventory	Multi-environment scanning, API key/secret detection	AppViewX, CyberArk, Keyfactor	Manual discovery may miss shadow identities.
Certificate Lifecycle Management	Automated issuance, revocation, monitoring	CLM tools, PKI-as-a-Service	Governance-heavy; skill-intensive.
Secret Management	Vaults, access controls, integration	HashiCorp Vault, cloud secret managers	Requires secure key handling.
Least Privilege / Access Governance	RBAC, minimal permissions, JIT access	IAM platforms, Zero Trust tools	Complex role mapping.
Monitoring & Anomaly Detection	Logging, usage tracking, alerts	SIEM/XDR integrations	False positives, tuning challenges.

Integrating Machine Identity Management with CI/CD / DevOps

Automate identity issuance during deployments.
Scan for embedded secrets and misconfigurations.
Use ephemeral credentials.
Store secrets securely within pipelines.

Monitoring, Alerting, Incident Recovery

Set up expiry alerts, anomaly detection, usage logging.
Define incident playbooks.
Plan for credential compromise and certificate revocation.

Roadmap & Metrics

Suggested Roadmap Phases

Baseline & Discovery
Policy & Ownership
Automate Key Controls
Monitoring & Audit
Resilience & Recovery
Continuous Improvement

Key Metrics To Track

Identity count and classification
Privilege levels and violations
Rotation and expiration timelines
Incidents involving machine credentials
Audit findings and policy compliance

More Info and Help

Need help mapping, securing, and governing your machine identities? MicroSolved has decades of experience helping organizations of all sizes assess and secure non-human identities across complex environments. We offer:

Machine Identity Risk Assessments
Lifecycle and PKI Strategy Development
DevOps and CI/CD Identity Integration
Secrets Management Solutions
Incident Response Planning and Simulations

References

* 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 September 3, 2025 by Brent Huston

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 August 25, 2025 by Brent Huston

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

KnowBe4. Infosecurity Europe 2025: Human Error & Cognitive Risk Findings. knowbe4.com
ITPro. Employee distraction is now your biggest cybersecurity risk. itpro.com
Sprinto. Trends in 2025 Cybersecurity Culture and Controls.
Deloitte Insights. Behavioral Nudges in Security Awareness Programs.
Axios & Wikipedia. AI-Generated Deepfakes and Psychological Manipulation Trends.
TechRadar. The Growing Threat of AI in Phishing & Vishing.
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 Complexity & Tool Sprawl in Security Operations

Posted on August 11, 2025 by Brent Huston

Security operations teams today are strained under the weight of fragmented, multi-vendor tool ecosystems that impede response times, obscure visibility, and generate needless friction.

ChatGPT Image Aug 11 2025 at 11 20 06 AM

Recent research paints a troubling picture: in the UK, 74% of companies rely on multi-vendor ecosystems, causing integration issues and inefficiencies. Globally, nearly half of enterprises now manage more than 20 tools, complicating alert handling, risk analysis, and streamlined response. Equally alarming, some organizations run 45 to 83 distinct cybersecurity tools, encouraging redundancy, higher costs, and brittle workflows.

Why It’s Urgent

This isn’t theoretical—it’s being experienced in real time. A recent MSP-focused study shows 56% of providers suffer daily or weekly alert fatigue, and 89% struggle with tool integration, driving operational burnout and missed threats. Security teams are literally compromised by their own toolsets.

What Organizations Are Trying

Many are turning to trusted channel partners and MSPs to streamline and unify their stacks into more cohesive, outcome-oriented infrastructures. Others explore unified platforms—for instance, solutions that integrate endpoint, user, and operational security tools under one roof, promising substantial savings over maintaining a fragmented set of point solutions.

Gaps in Existing Solutions

Despite these efforts, most organizations still lack clear, actionable frameworks for evaluating and rationalizing toolsets. There’s scant practical guidance on how to methodically assess redundancy, align tools to risk, and decommission the unnecessary.

A Practical Framework for Tackling Tool Sprawl

1. Impact of Tool Sprawl

Costs: Overlapping subscriptions, unnecessary agents, and complexity inflate spend.
Integration Issues: Disconnected tools produce siloed alerts and fractured context.
Alert Fatigue: Driven by redundant signals and fragmented dashboards, leading to slower or incorrect responses.

2. Evaluating Tool Value vs. Redundancy

Develop a tool inventory and usage matrix: monitor daily/weekly usage, overlap, and ROI.
Prioritize tools with high integration capability and measurable security outcomes—not just long feature lists.
Apply a complexity-informed scoring model to quantify the operational burden each tool introduces.

3. Framework for Decommissioning & Consolidation

Inventory all tools across SOC, IT, OT, and cloud environments.
Score each by criticality, integration maturity, overlap, and usage.
Pilot consolidation: replace redundant tools with unified platforms or channel-led bundles.
Deploy SOAR or intelligent SecOps solutions to automate alert handling and reduce toil.
Measure impact: track response time, fatigue levels, licensing costs, and analyst satisfaction before and after changes.

4. Case Study Sketch (Before → After)

Before: A large enterprise runs 60–80 siloed security tools. Analysts spend hours switching consoles; alerts go untriaged; budgets spiral.

After: Following tool rationalization and SOAR adoption, the tool count drops by 50%, alert triage automates 60%, response times improve, and operational costs fall dramatically.

5. Modern Solutions to Consider

SOAR Platforms: Automate workflows and standardize incident response.
Intelligent SecOps & AI-Powered SIEM: Provide context-enriched, prioritized, and automated alerts.
Unified Stacks via MSPs/Channel: Partner-led consolidation streamlines vendor footprint and reduces cost.

Conclusion: A Path Forward

Tool sprawl is no longer a matter of choice—it’s an operational handicap. The good news? It’s fixable. By applying a structured, complexity-aware framework, paring down redundant tools, and empowering SecOps with automation and visibility, SOCs can reclaim agility and effectiveness. In Brent Huston’s words: it’s time to simplify to secure—and to secure by deliberate design.

References

* 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 August 6, 2025 by Brent Huston

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.

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.