CaneCorso™ and the Real Problems AI Is Creating for the Business

Posted on April 22, 2026 by Brent Huston

AI didn’t sneak into the enterprise.

It walked in through productivity.

Email triage. Document handling. Support workflows. Internal copilots. Retrieval systems. Early agentic use cases. All of it made sense at the time. All of it still does.

But something changed along the way.

We didn’t just adopt AI—we embedded it into workflows that can influence decisions, expose data, and take action.

That’s where the problem starts.

And it’s exactly where CaneCorso™ is designed to operate.

CaneCorsoAI

AI Risk Isn’t a Model Problem — It’s a Workflow Problem

There’s a persistent misunderstanding in the market right now.

Most conversations about AI security still center on the model—what it knows, how it behaves, whether it can be tricked.

That’s not where the real risk lives.

The real risk shows up when:

Untrusted content enters a workflow
That workflow uses AI to interpret or transform it
And the output influences business operations

That content might come from:

Email
Documents
OCR pipelines
Retrieved knowledge (RAG)
Support tickets
External data sources

Once it’s in the workflow, it’s no longer just data.

It’s influence.

CaneCorso™ exists to control that influence—before it becomes an operational problem.

The Perimeter Moved — Most Organizations Didn’t

Traditional security models assume boundaries.

Applications. Networks. Endpoints. Users.

AI workflows don’t respect those boundaries.

They collapse:

Data
Instructions
Context
Intent

…into the same channel.

That creates an entirely different risk profile:

Prompt injection (direct and indirect)
Data exfiltration through prompt manipulation
RAG poisoning and retrieval contamination
Multimodal attacks through documents and images
Unsafe tool usage triggered by manipulated inputs

These are not theoretical edge cases.

They are natural outcomes of how AI is being used today.

CaneCorso™ addresses this by acting as a shared AI Application Firewall—a control layer that sits in front of real workflows, not just models.

Small Businesses: The Problem Is Safe Adoption

Small organizations aren’t trying to solve AI security academically.

They’re trying to use AI without breaking the business.

They typically don’t have:

Dedicated AI security engineering
Time to build custom controls
Resources to continuously test workflows

But they still face the same risks.

For them, the core problem is simple:

How do we use AI without creating exposure we don’t understand?

CaneCorso™ answers that by providing:

A reusable control layer
Business-safe handling decisions (allow, sanitize, tokenize, block)
Protection against injection and data leakage
Minimal disruption to workflow performance

The goal isn’t perfection.

It’s safe, practical adoption.

Mid-Size Organizations: The Problem Is Inconsistency

Mid-market firms hit a different wall.

AI use spreads quickly—but control does not.

You end up with:

One team securing prompts one way
Another team building ad hoc filters
A third team doing nothing at all

What looks like progress is actually fragmentation.

And fragmentation creates risk.

Because now:

Policies are inconsistent
Logging is inconsistent
Enforcement is inconsistent
Assurance is impossible

CaneCorso™ solves this by introducing a single control plane across workflows.

Not by replacing tools.

But by normalizing how risk is handled across:

Inputs
Prompts
Retrieved data
Outputs

That shift—from local fixes to shared control—is what enables real governance.

Enterprise: The Problem Is Scale and Assurance

Enterprises don’t struggle with whether to use AI.

They struggle with using it at scale without losing control.

The complexity shows up quickly:

More workflows
More data sources
More sensitive content
More downstream impact

Risk concentrates in places like:

Document ingestion pipelines
Retrieval systems
Internal copilots
Agent-driven workflows
Tool-connected AI systems

At that scale, the question changes.

It’s no longer:

“Are we protected?”

It becomes:

“Can we prove we are operating safely?”

CaneCorso™ addresses both sides:

Centralized protection across workflows
Measurable assurance through testing and auditable decisions

Because at enterprise scale, security without evidence is just opinion.

The Difference: Protect the Workflow Without Breaking It

This is where most approaches fail.

Traditional security thinking leans toward blocking.

If something looks suspicious, stop it.

That works—until it breaks the business.

AI workflows are different.

They require more nuance.

CaneCorso™ is built around that reality:

Allow when safe
Sanitize when needed
Tokenize when privacy matters
Block when necessary

That model matters.

Because the goal is not to stop work.

The goal is to keep safe work moving.

The Reality Behind the Threats

It’s easy to focus on the technical attacks:

Prompt injection
Indirect injection
Data exfiltration attempts
RAG poisoning
Tool abuse

But in practice, those attacks succeed because of how systems are built and used.

Developers concatenate untrusted input into prompts
Teams trust retrieved content without validation
Users paste sensitive data into workflows
Agent permissions expand faster than controls
Deployments happen without adversarial testing

These are normal behaviors.

CaneCorso™ works because it assumes those realities—not ideal conditions.

What Actually Changes

When organizations put a control layer like CaneCorso™ in place, the impact is operational.

Not theoretical.

You see:

Reduced likelihood of avoidable AI-driven incidents
Less sensitive data leakage
Fewer workflow failures from brittle controls
Faster, safer AI adoption
A clearer story for auditors, customers, and leadership

That last point matters more than most people realize.

Because AI isn’t just a technology decision anymore.

It’s a business trust decision.

Final Thoughts: Rational AI Security

There are two bad approaches to AI right now.

Move fast and ignore the risk.

Or lock everything down and lose the value.

Neither works.

What organizations actually need is a rational approach:

Small businesses need safe adoption
Mid-size businesses need consistency
Enterprises need scale and assurance

CaneCorso™ aligns with that reality.

Not by trying to “solve AI.”

But by solving the actual problem:

controlling how untrusted content influences real business workflows.

That’s the shift.

And it’s where AI security either becomes operational—or irrelevant.

More Info

To learn more, just give us a call at +1.614.351.1237, or drop us a line at info@microsolved.com. We’d love to walk you through how CaneCorso can help you secure the AI future of your business!

* 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.

Introducing CaneCorso: An AI Application Firewall Built for Real Workflows

Posted on April 16, 2026 by Brent Huston

AI has officially crossed the line from experiment to infrastructure.

Email flows into copilots. Documents feed RAG pipelines. Support tickets trigger agents that can take action. The convenience is real—and so is the risk.

What hasn’t caught up is security.

Most security models were built for a world where inputs were predictable and trust boundaries were well-defined. That world doesn’t exist anymore. Today, untrusted content flows directly into systems that can reason, decide, and act.

That’s exactly where things get interesting—and dangerous.

When Good Data Carries Bad Instructions

One of the biggest misconceptions about AI security is that it’s a model problem. It’s not. It’s a workflow problem.

Attackers don’t need to break in anymore. They ride along with legitimate data—emails, PDFs, tickets, knowledge base entries—and inject instructions that your AI system may interpret as truth.

Think about what that means in practice:

A support ticket that contains hidden instructions
A PDF with embedded prompt injection
A knowledge base entry that poisons RAG outputs
An approval workflow manipulated through summarization

Layer in human behavior—blind trust, over-privileged access, weak validation—and you’ve got a system primed to fail in ways that traditional controls simply won’t catch.

CaneCorsoAI

A More Rational Approach to AI Security

CaneCorso™ takes a different path.

Instead of trying to block everything suspicious (and breaking workflows in the process), it follows what’s described in the Rational AI Security model —security that behaves more like an immune system than a wall.

That means:

Detecting and isolating threats without stopping the system
Treating all inbound content as untrusted by default
Preserving business continuity while reducing risk
Producing measurable, auditable outcomes

This isn’t theoretical. It’s a direct response to how AI systems actually behave in production.

One Control Plane for AI Workflows

At its core, CaneCorso gives you a shared AI Application Firewall—a single control plane that sits between your workflows and your models.

Instead of every team building its own brittle filters, you get consistent, reusable protection across:

Email triage and analysis
RAG pipelines and knowledge systems
Document AI and OCR ingestion
Support and ticketing workflows
Agent-driven automation

The platform delivers:

Runtime decisions: allow, sanitize, tokenize, or block
Privacy controls: redact or tokenize sensitive data before model exposure
Audit-ready logs: reasons, scores, and evidence you can actually use
Adversarial validation: Injection Scanner proves controls before and after deployment

This isn’t just about stopping attacks—it’s about making security operationally usable.

How It Works (Without Breaking Everything)

CaneCorso is built around a simple but effective model:

Connect the workflow
Mailboxes, agents, or document pipelines send raw content through a single control point.
Evaluate risk
The system analyzes both security threats and privacy exposure in real time.
Apply the right action
Policies determine whether content is allowed, sanitized, tokenized, or blocked.
Keep work moving
Safe content continues downstream with context, scores, and auditability intact.

The key difference? It doesn’t rely on hard blocking as the default.

Inline tokenization replaces only the unsafe portion of content—meaning the workflow continues, the business operates, and the risk is neutralized.

Why This Matters Right Now

The perimeter has moved.

AI systems don’t just process data—they act on it. That turns every input into a potential control decision.

The threat landscape outlined in the workflow map highlights the shift:

Indirect prompt injection from internal or trusted sources
Multimodal attacks hidden in images, PDFs, or OCR text
Human-in-the-loop deception during approvals
Over-privileged workflows amplifying impact

These aren’t edge cases. They’re becoming normal operating conditions.

Measurable Security, Not Assumptions

One of the most important shifts CaneCorso introduces is moving security from belief to proof.

The Injection Scanner continuously tests workflows against adversarial scenarios, providing measurable evidence that controls work:

Before deployment
After changes
During audits or customer reviews

That matters for engineering teams. It matters for security teams. And it definitely matters when someone asks, “How do you know this is safe?”

Final Thoughts: Security That Matches Reality

For years, security teams have had to choose between protection and usability.

In the AI era, that trade-off doesn’t hold up.

CaneCorso is built on a simple idea: protect the workflow without breaking it. That means embracing how AI systems actually work—messy inputs, probabilistic outputs, and human decision-making in the loop.

If you’re deploying AI in any meaningful way, the question isn’t whether you’ll face these risks.

It’s whether you’ll be ready when you do.

Learn More

To learn more about CaneCorso, schedule a demo, or discuss your environment:

Email: info@microsolved.com
Phone: +1.614.351.1237
Explainer Video: Click Here To Watch Video

Rethinking Account Lockouts: Why 15 Minutes Isn’t a Strategy

Posted on April 6, 2026 by Brent Huston

There’s a moment in almost every security program where someone asks a deceptively simple question:

“Is 15 minutes a standard account lockout duration?”

The short answer? No.
The more honest answer? It’s common—but often wrong for the environment it’s deployed in.

And I’ve seen more than a few organizations learn that the hard way.

3Errors

The Myth of the “Standard” Lockout

If you go looking for authoritative guidance—from Center for Internet Security, FFIEC, or CISA—you’ll notice something interesting:

They don’t tell you what number to use.

Instead, they consistently emphasize:

Risk-based decision making
Balancing usability and security
Detecting and responding to threats—not just blocking them

That’s not an accident. It’s an acknowledgment that static controls like lockouts are blunt instruments in a very dynamic threat landscape.

What We Actually See in the Real World

Across environments—financial services, healthcare, SaaS, manufacturing—the patterns are pretty consistent:

Setting	Typical Range
Failed attempts before lockout	3–10
Lockout duration	5–30 minutes
Most common default	10–15 minutes

So yes, 15 minutes sits comfortably in the middle.

But “common” and “effective” are not the same thing.

Where 15 Minutes Breaks Down

1. It Punishes Users More Than Attackers

A 15-minute lockout sounds reasonable—until you multiply it.

A clinician locked out mid-shift
A call center agent missing SLAs
A trader unable to access systems during market hours

Now multiply that by repeated lockouts from cached credentials, mobile devices, or service accounts.

You don’t just have a security control—you have an operational problem.

2. It Doesn’t Stop Modern Attacks

Attackers have evolved. Most environments haven’t.

Today’s common attack patterns:

Password spraying (low-and-slow, avoids thresholds)
Credential stuffing (valid credentials, no lockout triggered)

A longer lockout duration doesn’t meaningfully impact either.

If anything, it gives a false sense of security while the real attack path goes untouched.

What Actually Works: A Layered Approach

This is where the conversation needs to shift—from “what’s the right number?” to “what’s the right strategy?”

1. Lockouts Are Supporting Controls—Not Primary Defenses

If you’re relying on lockouts as your main protection, you’re already behind.

At a minimum, you should be pairing with:

MFA everywhere it’s technically feasible
Conditional access (device, location, behavior)
Authentication throttling and smart detection

2. Tune for Risk, Not Defaults

A more balanced configuration tends to look like:

5–10 failed attempts
5–10 minute lockout
Reset counter after a defined cooldown window

This reduces user friction while still slowing down brute-force attempts.

More importantly—it acknowledges that lockouts are a speed bump, not a wall.

3. Progressive Delays Beat Hard Lockouts

One of the most underutilized strategies is progressive delay:

Attempts 1–2 → no delay
Attempts 3–5 → 30–60 second delay
Continued attempts → increasing delay

This approach:

Degrades attacker efficiency
Preserves user productivity
Avoids helpdesk spikes

It’s a far more surgical control than a blanket 15-minute lockout.

4. Detection Over Punishment

Modern security programs don’t just block—they observe.

You should be:

Logging all failed authentication attempts
Alerting on patterns (spraying, geographic anomalies)
Correlating identity signals across systems

Lockouts should be one signal among many—not the primary response.

Implementing This in Active Directory

Let’s get practical.

In on-prem Active Directory, you’re working primarily with Group Policy.

Recommended Baseline

In your domain or fine-grained password policy:

Account lockout threshold: 5–10 attempts
Account lockout duration: 5–10 minutes
Reset account lockout counter after: 10–15 minutes

Where to Configure

Group Policy Management Console (GPMC)
- Computer Configuration → Policies → Windows Settings → Security Settings → Account Policies → Account Lockout Policy

Advanced Considerations

Use Fine-Grained Password Policies (FGPP) for high-risk accounts (admins, service accounts)
Monitor Event IDs:
- 4625 (failed logon)
- 4740 (account locked out)
Feed logs into your SIEM for correlation and alerting

Implementing This in Microsoft 365

In Microsoft 365, the model shifts significantly.

You don’t directly control “lockout duration” in the same way—because the platform is already applying smart lockout behavior.

Smart Lockout (Azure AD / Entra ID)

Automatically tracks failed attempts
Uses adaptive thresholds
Differentiates between familiar and unfamiliar locations

What You Should Do Instead

1. Enable and Enforce MFA

Conditional Access → Require MFA for all users (with staged rollout if needed)

2. Configure Conditional Access Policies

Block legacy authentication
Require compliant devices
Apply geographic restrictions where appropriate

3. Monitor Identity Signals

Azure AD Sign-in logs
Risky sign-ins and users
Integration with Defender for Identity / Sentinel

4. Tune Smart Lockout (if needed)

Default threshold is typically sufficient
Adjust only if you have a strong operational reason

The Bottom Line

A 15-minute lockout isn’t wrong.

It’s just incomplete.

✔️ It’s common
❌ It’s not a standard
⚠️ It can create more operational pain than security value

The real shift is this:

Stop treating account lockouts as a primary control. Start treating them as part of a layered identity defense strategy.

Because in today’s environment, the goal isn’t just to block access.

It’s to understand it.

* 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.

Update on PromptDefense Suite and AI Security Research

Posted on March 23, 2026 by Brent Huston

Last week, I discussed why and some of how we built the new PromptDefense Suite.

This week, we are discussing the product’s future internally and how we might go to market. This is mainly due to two new capabilities we have built into the product.

The first is an API and workflow automation mechanism. This allows organizations to stand up a single instance of PromptDefense and then use it to protect multiple AI/agent workflows. The code no longer has to be embedded directly in the project; instead, all defensive capabilities and logging can be accessed via an API instance. The API is robust and supports API key restrictions that tie into a rules engine, so that different workflows can have different trust models and actions pre-assigned in an audit-friendly way.

Secondly, we have developed a licensing mechanism that covers protected workflows and skips the per-seat, per-token models that seemed too confusing for most firms looking for these kinds of tools. They told us they wanted a simpler licensing approach, and we developed a new licensing mechanism to make it easy, manageable, and auditable. Our testers have been calling it a win!

As we continue with the beta-testing process and lock down our decisions about where the product is going, the news that drove us to create it continues to flow in. More of our clients are working on agents and AI-integrated workflows, which require this level of protection. While we continue to develop PromptDefender, we are also working to develop and release extended frameworks for AI model, agent, and product management, along with policies, procedures, and vendor risk assessment tools for these frameworks, for our vCISO clients. We’re also busy researching ongoing compliance implementation for AI workflows and agents, and should have more on that shortly.

In the meantime, if you want to discuss AI or agent security, risk management, or other relevant topics, please reach out. We would love to talk with you and help align our modernization capabilities with your emerging needs. You can always email us at info@microsolved.com or call us at +1-614-351-1237.

As always, thanks for reading. Stay safe out there, and stay tuned for more updates.

The Hidden Cost of Compliance: Why “Checkbox Security” Fails Modern Organizations

Posted on February 9, 2026 by Brent Huston

In today’s threat landscape, simply “checking the boxes” isn’t enough. Organizations invest enormous time and money to satisfy regulatory frameworks like PCI DSS, HIPAA, ISO 27001, GDPR, and NIS2—but too often they stop there. The result? A false sense of cybersecurity readiness that leaves critical vulnerabilities unaddressed and attackers unchallenged.

Compliance should be a foundation—not a finish line. Let’s unpack why checkbox compliance consistently fails modern enterprises and how forward-looking security leaders can close the gap with truly risk-based strategies.

Compliance vs. Security: Two Sides of the Same Coin?

Compliance and security are related—but they are emphatically not the same thing.

Compliance is about adherence to external mandates, standards, and audits.
Security is about reducing risk, defending against threats, and protecting data, systems, and business continuity.

Expecting compliance alone to prevent breaches is like believing that owning a fire extinguisher will stop every fire. The checklists in PCI DSS, HIPAA, or ISO standards are minimum controls designed to reduce loss—not exhaustive defenses against every attacker tactic.

“Compliance is not security.” — Security thought leaders have said this many times, and it rings true as organizations equate audit success with risk reduction.

Checkbox Security: Why It Fails

A compliance mindset often devolves into a checkbox mentality—complete documentation, filled-in forms, and green lights from auditors. But this approach contains several fundamental flaws:

1. Compliance Standards Lag Behind Evolving Threats

Most regulatory frameworks are reactive, built around known threats and past incidents. Cyber threats evolve constantly; sticking strictly to compliance means protecting against yesterday’s risks, not today’s or tomorrow’s.

2. Checklists Lack Contextual Risk Prioritization

Compliance is binary—yes/no answers. But not all controls have equal impact. A firewall might be present (box ticked), yet the organization might ignore the most actively exploited vulnerabilities like unpatched software or phishing risk.

3. Audit Success Doesn’t Equal Real-World Security

Auditors assess documentation and evidence of controls; they rarely test adversarial resilience. A compliant organization can still suffer devastating breaches because compliance assessments aren’t adversarial and don’t simulate real attacks.

Real-World Proof: Breaches Despite Compliance

Arguments against checkbox compliance sound theoretical—until you look at real breaches. Examples of organizations meeting compliance requirements yet being breached are widespread:

PCI DSS Compliance Breaches

Despite strict PCI requirements for safeguarding cardholder data, many breached organizations were technically compliant at the time of compromise. Researchers even note that no fully compliant organization examined was breach-free, and compliance fines or gaps didn’t prevent attackers from exploiting weak links in implementation.

Healthcare Data Risks Despite HIPAA

Even with stringent HIPAA requirements, healthcare breaches are rampant. Reports show thousands of HIPAA violations and data exposures annually, demonstrating that merely having compliance frameworks doesn’t stop attackers.

The Hidden Costs of Compliance-Only Security

When organizations chase compliance without aligning to deeper risk strategy, the costs go far beyond audit efforts.

1. Opportunity Cost

Security teams spend incredible hours on documentation, standard operating procedure updates, and audit response—hours that could otherwise support vulnerability remediation, threat hunting, and continuous monitoring.

2. False Sense of Security

Executives and boards often equate compliance with safety. But compliance doesn’t guarantee resilience. That false confidence can delay investments in deeper controls until it’s too late.

3. Breach Fallout

When conformity fails, consequences extend far beyond compliance fines. Reputational damage, customer churn, supply chain impacts, and board-level accountability can dwarf regulatory penalties.

Beyond Checkboxes: What Modern Security Needs

To turn compliance from checkbox security into business-aligned risk reduction, organizations should consider the following advanced practices:

1. Continuous Risk Measurement

Shift from periodic compliance assessments to continuous risk evaluation tied to real business outcomes. Tools that quantify risk exposure in financial and operational terms help prioritize investments where they matter most.

2. Threat Modeling & Adversary Emulation

Map attacker tactics relevant to your business context, then test controls against them. Frameworks like MITRE ATT&CK can help organizations think like attackers, not auditors.

3. Metrics That Measure Security Effectiveness

Move away from compliance metrics (“% of controls implemented”) to outcome metrics (“time to detect/respond to threats,” “reduction in high-risk exposures,” etc.). These demonstrate real improvements versus checkbox completion.

4. Integration of Security and Compliance

Security leaders should leverage compliance requirements as part of broader risk strategy—not substitutes. GRC (Governance, Risk, and Compliance) platforms can tie compliance evidence to risk dashboards for a unified view.

How MicroSolved Can Help

At MicroSolved, we’ve seen these pitfalls firsthand. Organizations often approach compliance automation or external consultants expecting silver bullets—but without continuous risk measurement and business context, security controls still fall short.

MicroSolved’s approach focuses on:

Risk-based security program development
Ongoing threat modeling and adversary testing
Metrics and dashboards tied to business outcomes
Integration of compliance frameworks like PCI, HIPAA, ISO 27001 with enterprise risk strategies

If your team is struggling to move beyond checkbox compliance, we’re here to help align your cybersecurity program with real-world risk reduction—not just regulatory requirements.

➡️ Learn more about how MicroSolved can help bridge the gap between compliance and true security effectiveness.

Conclusion: Compliance Is the Floor, Not the Ceiling

Regulatory frameworks remain essential—they set the minimum expectations for protecting data and privacy. But in a world of rapidly evolving threats, compliance alone can’t be the endpoint of your cybersecurity efforts.

Checkbox security gives boards comfort, but attackers don’t check boxes—they exploit gaps.

Security leaders who integrate risk measurement, continuous validation, and business alignment into their compliance programs not only strengthen defenses—they elevate security into a source of competitive advantage.

* 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.

Modernizing Compliance: An OSCAR-Inspired Approach to Automation for Credit Unions in 2026

Posted on December 29, 2025 by Brent Huston

As credit unions navigate an increasingly complex regulatory landscape in 2026—balancing cybersecurity mandates, fair lending requirements, and evolving privacy laws—the case for modern, automated compliance operations has never been stronger. Yet many small and mid-sized credit unions still rely heavily on manual workflows, spreadsheets, and after-the-fact audits to stay within regulatory bounds.

To meet these challenges with limited resources, it’s time to rethink how compliance is operationalized—not just documented. And one surprising source of inspiration comes from a system many credit unions already touch: e‑OSCAR.

E compliance

What Is “OSCAR-Style” Compliance?

The e‑OSCAR platform revolutionized how credit reporting disputes are processed—automating a once-manual, error-prone task with standardized electronic workflows, centralized audit logs, and automated evidence generation. That same principle—automating repeatable, rule-driven compliance actions and connecting systems through a unified, traceable framework—can and should be applied to broader compliance areas.

An “OSCAR-style” approach means moving from fragmented checklists to automated, event-driven compliance workflows, where policy triggers launch processes without human lag or ambiguity. It also means tighter integration across systems, real-time monitoring of risks, and ready-to-go audit evidence built into daily operations.

Why Now? The 2026 Compliance Pressure Cooker

For credit unions, 2026 brings a convergence of pressures:

New AI and automated decision-making laws (especially at the state level) require detailed documentation of how member data and lending decisions are handled.
BSA/AML enforcement is tightening, with regulators demanding faster responses and proactive alerts.
NCUA is signaling closer cyber compliance alignment with FFIEC’s CAT and other maturity models, especially in light of public-sector ransomware trends.
Exam cycles are accelerating, and “show your work” now means “prove your controls with logs and process automation.”

Small teams can’t keep up with these expectations using legacy methods. The answer isn’t hiring more staff—it’s changing the model.

The Core Pillars of an OSCAR-Inspired Compliance Model

Event-Driven Automation
Triggers like a new member onboarding, a flagged transaction, or a regulatory update initiate prebuilt compliance workflows—notifications, actions, escalations—automatically.
Standardized, Machine-Readable Workflows
Compliance obligations (e.g., Reg E, BSA alerts, annual disclosures) are encoded as reusable processes—not tribal knowledge.
Connected Systems & Data Flows
APIs and batch exchanges tie together core banking, compliance, cybersecurity, and reporting systems—just like e‑OSCAR connects furnishers and bureaus.
Real-Time Risk Detection
Anomalies and policy deviations are detected automatically and trigger workflows before they become audit findings.
Automated Evidence & Audit Trails
Every action taken is logged and time-stamped, ready for examiners, with zero manual folder-building.

How Credit Unions Can Get Started in 2026

1. Begin with Your Pain Points
Where are you most at risk? Where do tasks fall through the cracks? Focus on high-volume, highly regulated areas like BSA/AML, disclosures, or cybersecurity incident reporting.

2. Inventory Obligations and Map to Triggers
Define the events that should launch compliance workflows—new accounts, flagged alerts, regulatory updates.

3. Pilot Automation Tools
Leverage low-code workflow engines or credit-union-friendly GRC platforms. Ensure they allow for API integration, audit logging, and dashboard oversight.

4. Shift from “Tracking” to “Triggering”
Replace compliance checklists with rule-based workflows. Instead of “Did we file the SAR?” it’s “Did the flagged transaction automatically escalate into SAR review with evidence attached?”

✅ More Info & Help: Partner with Experts to Bring OSCAR-Style Compliance to Life

Implementing an OSCAR-inspired compliance framework may sound complex—but you don’t have to go it alone. Whether you’re starting from a blank slate or evolving an existing compliance program, the right partner can accelerate your progress and reduce risk.

MicroSolved, Inc. has deep experience supporting credit unions through every phase of cybersecurity and compliance transformation. Through our Consulting & vCISO (Virtual Chief Information Security Officer) program, we provide tailored, hands-on guidance to help:

Assess current compliance operations and identify automation opportunities
Build strategic roadmaps and implementation blueprints
Select and integrate tools that match your budget and security posture
Establish automated workflows, triggers, and audit systems
Train your team on long-term governance and resilience

Whether you’re responding to new regulatory pressure or simply aiming to do more with less, our team helps you operationalize compliance without overloading staff or compromising control.

📩 Ready to start your 2026 planning with expert support?
Visit www.microsolved.com or contact us directly at info@microsolved.com to schedule a no-obligation strategy call.

* 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.

Non-Human Identities & Agentic Risk:

Posted on December 1, 2025 by Brent Huston

The Security Implications of Autonomous AI Agents in the Enterprise

Over the last year, we’ve watched autonomous AI agents — not the chatbots everyone experimented with in 2023, but actual agentic systems capable of chaining tasks, managing workflows, and making decisions without a human in the loop — move from experimental toys into enterprise production. Quietly, and often without much governance, they’re being wired into pipelines, automation stacks, customer-facing systems, and even security operations.

And we’re treating them like they’re just another tool.

They’re not.

These systems represent a new class of non-human identity: entities that act with intent, hold credentials, make requests, trigger processes, and influence outcomes in ways we previously only associated with humans or tightly-scoped service accounts. But unlike a cron job or a daemon, today’s AI agents are capable of learning, improvising, escalating tasks, and — in some cases — creating new agents on their own.

That means our security model, which is still overwhelmingly human-centric, is about to be stress-tested in a very real way.

Let’s unpack what that means for organizations.

WorkingWithRobot1

Why AI Agents Must Be Treated as Identities

Historically, enterprises have understood identity in human terms: employees, contractors, customers. Then we added service accounts, bots, workloads, and machine identities. Each expansion required a shift in thinking.

Agentic AI forces the next shift.

These systems:

Authenticate to APIs and services
Consume and produce sensitive data
Modify cloud or on-prem environments
Take autonomous action based on internal logic or model inference
Operate 24/7 without oversight

If that doesn’t describe an “identity,” nothing does.

But unlike service accounts, agentic systems have:

Adaptive autonomy – they make novel decisions, not just predictable ones
Stateful memory – they remember and leverage data over time
Dynamic scope – their “job description” can expand as they chain tasks
Creation abilities – some agents can spawn additional agents or processes

This creates an identity that behaves more like an intern with root access than a script with scoped permissions.

That’s where the trouble starts.

What Could Go Wrong? (Spoiler: A Lot)

Most organizations don’t yet have guardrails for agentic behavior. When these systems fail — or are manipulated — the impacts can be immediate and severe.

1. Credential Misuse

Agents often need API keys, tokens, or delegated access.
Developers tend to over-provision them “just to get things working,” and suddenly you’ve got a non-human identity with enough privilege to move laterally or access sensitive datasets.

2. Data Leakage

Many agents interact with third-party models or hosted pipelines.
If prompts or context windows inadvertently contain sensitive data, that information can be exposed, logged externally, or retained in ways the enterprise can’t control.

3. Shadow-Agent Proliferation

We’ve already seen teams quietly spin up ChatGPT agents, GitHub Copilot agents, workflow bots, or LangChain automations.

In 2025, shadow IT has a new frontier:
Shadow agents — autonomous systems no one approved, no one monitors, and no one even knows exist.

4. Supply-Chain Manipulation

Agents pulling from package repositories or external APIs can be tricked into consuming malicious components. Worse, an autonomous agent that “helpfully” recommends or installs updates can unintentionally introduce compromised dependencies.

5. Runaway Autonomy

While “rogue AI” sounds sci-fi, in practice it looks like:

An agent looping transactions
Creating new processes to complete a misinterpreted task
Auto-retrying in ways that amplify an error
Overwriting human input because the policy didn’t explicitly forbid it

Think of it as automation behaving badly — only faster, more creatively, and at scale.

A Framework for Agentic Hygiene

Organizations need a structured approach to securing autonomous agents. Here’s a practical baseline:

1. Identity Management

Treat agents as first-class citizens in your IAM strategy:

Unique identities
Managed lifecycle
Documented ownership
Distinct authentication mechanisms

2. Access Control

Least privilege isn’t optional — it’s survival.
And it must be dynamic, since agents can change tasks rapidly.

3. Audit Trails

Every agent action must be:

Traceable
Logged
Attributable

Otherwise incident response becomes guesswork.

4. Privilege Segregation

Separate agents by:

Sensitivity of operations
Data domains
Functional responsibilities

An agent that reads sales reports shouldn’t also modify Kubernetes manifests.

5. Continuous Monitoring

Agents don’t sleep.
Your monitoring can’t either.

Watch for:

Unexpected behaviors
Novel API call patterns
Rapid-fire task creation
Changes to permissions
Self-modifying workflows

6. Kill-Switches

Every agent must have a:

Disable flag
Credential revocation mechanism
Circuit breaker for runaway execution

If you can’t stop it instantly, you don’t control it.

7. Governance

Define:

Approval processes for new agents
Documentation expectations
Testing and sandboxing requirements
Security validation prior to deployment

Governance is what prevents “developer convenience” from becoming “enterprise catastrophe.”

Who Owns Agent Security?

This is one of the emerging fault lines inside organizations. Agentic AI crosses traditional silos:

Dev teams build them
Ops teams run them
Security teams are expected to secure them
Compliance teams have no framework to govern them

The most successful organizations will assign ownership to a cross-functional group — a hybrid of DevSecOps, architecture, and governance.

Someone must be accountable for every agent’s creation, operation, and retirement.
Otherwise, you’ll have a thousand autonomous processes wandering around your enterprise by 2026, and you’ll only know about a few dozen of them.

A Roadmap for Enterprise Readiness

Short-Term (0–6 months)

Inventory existing agents (you have more than you think).
Assign identity profiles and owners.
Implement basic least-privilege controls.
Create kill-switches for all agents in production.

Medium-Term (6–18 months)

Formalize agent governance processes.
Build centralized logging and monitoring.
Standardize onboarding/offboarding workflows for agents.
Assess all AI-related supply-chain dependencies.

Long-Term (18+ months)

Integrate agentic security into enterprise IAM.
Establish continuous red-team testing for agentic behavior.
Harden infrastructure for autonomous decision-making systems.
Prepare for regulatory obligations around non-human identities.

Agentic AI is not a fad — it’s a structural shift in how automation works.
Enterprises that prepare now will weather the change. Those that don’t will be chasing agents they never knew existed.

More Info & Help

If your organization is beginning to deploy AI agents — or if you suspect shadow agents are already proliferating inside your environment — now is the time to get ahead of the risk.

MicroSolved can help.
From enterprise AI governance to agentic threat modeling, identity management, and red-team evaluations of AI-driven workflows, MSI is already working with organizations to secure autonomous systems before they become tomorrow’s incident reports.

For more information or to talk through your environment, reach out to MicroSolved.
We’re here to help you build a safer, more resilient future.

* 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.

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.

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

Posted on July 31, 2025 by Brent Huston

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.

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

Title & Hook: “AI Isn’t Coming. It’s Here.”
Risk-Opportunity Matrix: Visual quadrant.
Use-Cases & Value: Case studies.
Top Risks & Incidents: Real-world examples.
Governance Framework: Your structure.
Policy Templates: Categories and value.
Training & Audit Plan: Timeline & roles.
Monitoring Dashboard: Your KPIs.
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.