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Great Women in Compliance

Great Women in Compliance: Risk as a Leadership Discipline: Lessons from Internal Audit

Guest Bio:

Michelle Wagner is Vice President and Head of Internal Audit at DocuSign, where she leads global audit strategy and helps the organization strengthen governance, risk management, and internal controls while supporting a culture of integrity and accountability.

With more than 25 years of experience across consulting and industry,

Michelle has held leadership roles at Deloitte, Costco, and SAP, where she led large audit portfolios, built high-performing teams, and drove governance and risk transformation initiatives across complex global organizations.

Michelle is known for her practical, people-centered approach to risk leadership and for translating complex risk insights into clear, actionable guidance. She is passionate about mentoring emerging leaders and helping organizations move from reactive risk management to proactive, insight-driven decision-making.

Show Notes:

Risk is often framed as technical work, but at its core, it is deeply human.

In this episode of Great Women in Compliance, Dr. Hemma Lomax sits down with Michelle Wagner, Head of Internal Audit at DocuSign, to explore how curiosity, empathy, and partnership help organizations manage risk more effectively and build stronger ethical cultures.

Michelle shares insights from a career spanning consulting and global leadership roles, reflecting on the moments that shaped her leadership philosophy and the lessons she has learned about influencing without authority, building trust, and helping teams see risks as opportunities to improve rather than problems to avoid.

Together, they discuss the evolving role of internal audit, the importance of collaboration across risk functions, and how emerging technologies such as AI can help leaders identify patterns and generate insights while reinforcing the need for human judgment.

This conversation is a reminder that great risk leaders don’t just protect organizations — they help them succeed.

Episode highlights:

  • Why risk management is fundamentally a leadership discipline
  • Lessons from moving from consulting to executive leadership roles
  • What makes an internal audit function truly valuable
  • How audit, compliance, and business teams can partner effectively
  • The role of curiosity and psychological safety in surfacing risks
  • Michelle’s perspective on AI and the future of risk management
  • Leadership lessons from mentoring and building teams
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AI Today in 5

AI Today in 5: April 28, 2026, The Barriers to Success in AI Edition

Welcome to AI Today in 5, the newest addition to the Compliance Podcast Network. Each day, Tom Fox will bring you 5 stories about AI to start your day. Sit back, enjoy a cup of morning coffee, and listen in to the AI Today In 5. All, from the Compliance Podcast Network. Each day, we consider five stories from the business world, compliance, ethics, risk management, leadership, or general interest about AI.

Top AI stories include:

  1. Governance and compliance barriers to AI success. (SC Media)
  2. AI in payroll. (Thomson Reuters)
  3. Can AI agents create regulatory risk? (ICAEW Insights)
  4. China blocks Meta takeover of Manus. (CNBC)
  5. OpenAI breaks Microsoft exclusivity. (Reuters)

For more information on the use of AI in compliance programs, Tom Fox’s new book, Upping Your Game, is available. You can purchase a copy of the book on Amazon.com.

To learn about the intersection of Sherlock Holmes and the modern compliance professional, check out Tom’s latest book, The Game is Afoot-What Sherlock Holmes Teaches About Risk, Ethics and Investigations on Amazon.com.

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Blog

René Descartes and the Discipline of Internal Investigation

This week, we are moving to Enlightenment Thinkers to see their influence on modern compliance programs. This week’s category is broader than philosophers, as many of these men excelled in numerous fields such as science, mathematics, calculus, and medicine. However, each contributed a key component that relates directly to our modern compliance regimes. In this post, we consider René Descartes and what he teaches as the next step beyond Bacon: that evidence must be rigorously examined.

If Francis Bacon taught us that a compliance program must be grounded in evidence, René Descartes teaches the next step: evidence must be examined with rigor. That is why Descartes is the natural second installment in this series on what Enlightenment thinkers can teach us about modern corporate compliance. Bacon gave us empiricism. Descartes gives us a method. Bacon tells us to look. Descartes tells us how to think about what we find.

For the compliance professional, that is no small matter. Modern compliance programs do not fail only because they lack information. They often fail because organizations do not ask the right questions, challenge convenient assumptions, or investigate troubling facts with sufficient discipline. A hotline report comes in, and management prematurely dismisses it. A financial anomaly is explained away because the business result looks attractive. A third-party red flag is rationalized because the market opportunity seems too important to slow down. In each case, the problem is not simply a lack of data. The problem is a lack of disciplined inquiry.

That is where Descartes has something important to say to the modern Chief Compliance Officer.

Why Descartes Matters to Compliance

René Descartes is best known for methodical doubt. He believed that if one wanted to arrive at reliable knowledge, one had to strip away weak assumptions and test what could be known. He did not advocate doubt for its own sake. He advocated doubt as a disciplined tool, a way to avoid error and reach sound conclusions. His method required breaking problems into parts, analyzing them carefully, proceeding in an orderly manner, and ensuring nothing important was overlooked. That is remarkably close to what an effective compliance investigation function should do.

The compliance professional cannot assume an allegation is false because it is inconvenient. Nor can one assume it is true because it is emotionally compelling. The task is to examine. What happened? Who knew what, and when? What documents exist? What controls should have operated? Where are the inconsistencies? What explanation fits the evidence, and what explanation merely sounds comforting? Descartes would have recognized this immediately. A sound conclusion requires method, not instinct.

In a corporate environment, that is especially important because organizations are full of narratives. Managers tell stories about performance. Employees tell stories about why something was necessary. Third parties tell stories about local customs or business necessities. The compliance function should listen, but it cannot stop there. It must test those stories against facts.

The DOJ Expects More Than a Quick Answer

The Department of Justice’s Evaluation of Corporate Compliance Programs (ECCP) does not use philosophical language, but its expectations align closely with Cartesian thinking. The ECCP asks whether investigations are properly scoped, whether the company has adequate resources to conduct them, whether the company preserves and analyzes relevant data, whether reporting structures support independence, and whether lessons learned are used to improve the compliance program. That is not a request for superficial closure. It is a demand for disciplined inquiry.

The ECCP is not interested in whether a company can produce a memo that says the matter has been reviewed. It wants to know whether the review was credible. Did the company ask hard questions? Did it follow the evidence even when the evidence was uncomfortable? Did it look at underlying causes or accept a narrow explanation that minimized institutional responsibility? These are Descartes’ questions as much as the DOJ’s.

Method Beats Reaction

One of the most important lessons Descartes offers is that method matters more than reaction. Too many organizations still respond to reports of misconduct in an ad hoc fashion. The identity of the reporter, the subject’s seniority, or the business sensitivity of the issue can distort the process from the outset. Some matters are overreacted to because they are visible. Others are under-investigated because they are politically awkward. That is not a system. That is improvisation. A mature compliance program requires a clear, repeatable investigative method.

That begins with triage. Allegations should be assessed based on risk, scope, subject matter, and potential impact. Matters involving senior leadership, financial controls, corruption risk, retaliation, or systemic process failures may require immediate escalation and greater independence. Low-risk issues may still require attention, but not every matter needs the same level of response. Cartesian thinking does not mean treating every problem identically. It means applying a coherent method to determine what level of inquiry is warranted.

From there, the matter should be broken down into manageable components. What is the allegation? What business process is implicated? What documents are likely relevant? Who are the key custodians? What data sources exist? What is the working timeline? What controls should have operated? What policy provisions may have been implicated? This is classic Descartes: divide complex problems into smaller parts so they can be understood.

Disciplined Skepticism Is a Compliance Strength

Compliance professionals sometimes worry that skepticism will be perceived as mistrust. But disciplined skepticism is not cynicism. It is not hostility. It is professional rigor. It is the recognition that people often explain events in self-protective ways, that organizations prefer neat stories to messy truths, and that important facts are often buried inside routine processes. Descartes would have understood that skepticism is a necessary safeguard against error.

Consider a common internal reporting scenario. A manager says that a questionable payment was simply an administrative oversight. Perhaps that is true. But a compliance professional guided by Descartes would ask several follow-up questions. Was it really isolated? Have similar payments occurred before? Were approval thresholds bypassed? Was the vendor properly vetted? Were invoice descriptions vague or coded? Did someone raise concerns earlier? Was the explanation consistent across all available records? None of those questions accuse. They clarify.

Documentation Turns Inquiry Into Credibility

Another Cartesian lesson for compliance is the importance of orderly reasoning. An investigation cannot simply be sound in substance. It must also be documented in a way that shows how the conclusion was reached. This is essential for institutional memory, for regulatory defensibility, and for credibility with boards and senior management.

A well-documented investigation answers basic but vital questions. What was alleged? Who handled the matter? What evidence was reviewed? Which witnesses were interviewed? What facts were established? What policy or control failures were identified? What conclusion was reached, and why? What remediation followed? This kind of documentation is not bureaucratic excess. It is proof of intellectual discipline.

Without it, the company cannot show that it acted reasonably. It cannot identify patterns across matters. It cannot demonstrate consistency. It cannot revisit earlier decisions when new facts emerge. Most importantly, it cannot turn an individual case into organizational learning. Descartes’ method was about structured thinking. In corporate compliance, documentation is how structured thinking becomes durable.

Independence Matters When the Facts Get Uncomfortable

No discussion of investigations would be complete without addressing independence. The most elegant methodology in the world will not help if investigators are pressured to protect favored executives, minimize business disruption, or avoid awkward findings. Cartesian rigor requires a willingness to follow the facts wherever they lead. That, in turn, requires real autonomy.

The ECCP addresses this directly through its focus on stature, authority, resources, and access. Can the compliance function investigate senior personnel? Can it escalate concerns to the board or audit committee when necessary? Is it empowered to challenge management narratives? These are not secondary governance questions. They are central to whether the investigation process can produce reliable conclusions.

There is a reason so many compliance failures involve not merely misconduct, but management interference with the review of misconduct. When power shapes the investigation, facts become negotiable. Descartes would have seen that as a fundamental corruption of method.

Investigations Must Lead to Remediation

A Cartesian compliance program does not end with a finding. It asks what the finding means for the system. That is why investigations must connect to remediation and root cause analysis. If an allegation is substantiated, the question is not simply who violated what rule. The question is what enabled the failure.

Was the training insufficient? Were incentives pushing employees toward bad decisions? Was a manager creating pressure that undermined ethical judgment? Did the approval process invite shortcuts? Was the policy too vague to guide real-world conduct? These questions push the company from conclusion to improvement.

This is where Descartes connects back to Bacon. Bacon teaches that we need evidence. Descartes teaches that we must reason carefully from the evidence. Together, they create a powerful model for compliance effectiveness. The company observes, investigates, documents, learns, and improves.

The Compliance Officer as a Guardian of Clear Thinking

If Bacon cast the compliance officer as an institutional scientist, Descartes casts the compliance officer as a guardian of clear thinking. In a corporation full of pressure, narrative, hierarchy, and urgency, that role is vital. Someone must insist that facts be tested, that assumptions be challenged, that conclusions be explained, and that the process remain disciplined when the easier path is to settle for a quick answer.

That is not merely an investigative skill. It is a governance function. It protects employee fairness, the board’s credibility, and the company’s defensibility. It also builds trust over time, because people learn that reports are taken seriously, that outcomes are reasoned rather than political, and that the system values truth over convenience.

René Descartes may seem an unlikely guide for corporate compliance. Yet his method of doubt, order, and careful reasoning belongs squarely within the modern best-practices compliance program. In an era where companies are judged not simply on whether they responded, but on how they responded, Descartes offers an enduring lesson: clear thinking is a control.

Five Lessons Learned for the Modern Compliance Professional

First, allegations should trigger a method, not a reaction. A repeatable investigative framework reduces bias and improves consistency.

Second, disciplined skepticism is a professional obligation. Compliance must test explanations against facts rather than accept convenient narratives.

Third, complex matters should be broken into parts. Scoping, evidence review, interviews, control mapping, and timeline construction all improve rigor.

Fourth, documentation is essential. It is how the company proves that its inquiry was credible and how it preserves institutional learning.

Fifth, an investigation is not complete until it informs remediation. Findings should lead to enhancements in control, policy changes, training updates, or broader governance improvements.

Coming Next: John Locke and the Legitimacy of Compliance Governance

If Francis Bacon teaches us to gather evidence and René Descartes teaches us to examine it rigorously, John Locke asks an equally important question: why should anyone trust the system in the first place? In Part 3, I will explore how Locke’s ideas about legitimacy, rights, and accountable authority provide a powerful framework for speak-up culture, non-retaliation, fairness, and board oversight. In the world of compliance, authority alone is never enough. It must also be credible.

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AI Today in 5

AI Today in 5: April 27, 2026, The AI Takes Over Retail Edition

Welcome to AI Today in 5, the newest addition to the Compliance Podcast Network. Each day, Tom Fox will bring you 5 stories about AI to start your day. Sit back, enjoy a cup of morning coffee, and listen in to the AI Today In 5. All, from the Compliance Podcast Network. Each day, we consider five stories from the business world, compliance, ethics, risk management, leadership, or general interest about AI.

Top AI stories include:

  1. Current status of state AI laws. (Cooley)
  2. Building defensible intelligence into your workflow. (Wolters Kluwer)
  3. Otter.ai is under legal scrutiny. (UC Today)
  4. AI takes over a store. (Bloomberg)
  5. Will Junior talent disrupt Goldman Sachs? (Business Insider)

For more information on the use of AI in compliance programs, Tom Fox’s new book, Upping Your Game, is available. You can purchase a copy of the book on Amazon.com.

To learn about the intersection of Sherlock Holmes and the modern compliance professional, check out Tom’s latest book, The Game is Afoot-What Sherlock Holmes Teaches About Risk, Ethics and Investigations on Amazon.com.

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Blog

Enlightenment Philosophers Week: Part 1 – Francis Bacon and the Compliance Program That Works in Practice

I have explored the work of ancient Greek and Roman philosophers to understand the underpinnings of the modern corporate compliance program. This week, I want to move to Enlightenment Thinkers. Our category is broader than that of philosophers, as many of these men excelled in numerous fields, including science, mathematics, calculus, and medicine. However, each contributed a key component that relates directly to our modern compliance regimes.

The five we will explore are Francis Bacon, René Descartes, John Locke, Thomas Hobbes, and Issac Newton. Today, we begin with Francis Bacon and the design of a compliance program that works not simply in theory but in practice.

There is a reason Francis Bacon is the right place to begin a series on what Enlightenment thinkers can teach us about modern corporate compliance. Bacon did not simply advance a philosophical idea. He changed the way serious people were supposed to think. He pushed inquiry away from inherited assumptions and abstract theorizing and toward observation, testing, evidence, and disciplined learning from experience. In many ways, that is the same journey corporate compliance has had to take.

For too long, compliance programs were judged by what they had on paper. Did the company have a code of conduct? Did it conduct annual training? Did it maintain a hotline? Did it have policies and procedures? Those questions still matter, of course, but they are no longer enough. The Department of Justice has made that point repeatedly through its Evaluation of Corporate Compliance Programs. The DOJ does not simply ask whether a company has a program. It asks whether the program is well designed, whether it is being applied earnestly and in good faith, and whether it works in practice. That final phrase could have been written by Bacon himself.

Why Bacon Matters to Compliance

Francis Bacon is most closely associated with empiricism, the idea that knowledge should be grounded in observation and experience rather than assumption or pure deduction. He believed that if you want to understand the world, you do not begin with what you hope is true. You begin with facts. You gather information. You test propositions. You challenge your own biases. Then you refine your conclusions based on the evidence. That mindset is at the heart of every effective compliance program.

A Chief Compliance Officer cannot assume that a policy is effective because it was well-drafted. A board cannot assume that a training program changes behavior because employees clicked through an online module. A legal department cannot assume that third-party due diligence is functioning because questionnaires are being completed. In each case, the real question is Baconian: what evidence do you have that the control is working as intended?

This is where philosophy becomes practice. Bacon gives compliance professionals a method. He reminds us that the difference between performative compliance and effective compliance is proof.

The DOJ Standard Is a Baconian Standard

The modern DOJ approach is deeply consistent with Bacon’s philosophy. The ECCP has moved the compliance conversation away from formalism and toward effectiveness. Prosecutors are instructed to consider whether a company has access to relevant data, whether it uses that data to monitor performance, whether it investigates red flags, whether it adapts the program based on lessons learned, and whether it performs root-cause analysis after misconduct occurs. That is not a paper exercise. That is evidence-based governance.

The DOJ is effectively saying that compliance must be a living system of observation, testing, response, and continuous improvement. In Bacon’s world, knowledge advances by disciplined interaction with reality. In the DOJ’s world, compliance credibility advances the same way. A company earns trust not because it announces a program, but because it can demonstrate through data, testing, and response that the program actually functions.

From Risk Assessment to Real Measurement

A Bacon-inspired compliance program begins with risk assessment, but it does not end there. Too many organizations treat the risk assessment as an annual exercise that produces a polished heat map and then disappears into a slide deck. Bacon would reject that approach. A risk assessment should be a working hypothesis about where misconduct and control failure are most likely to occur. That hypothesis must then be tested through monitoring, internal reporting, auditing, and data review.

Consider a company that identifies third-party risk as a top concern. A paper-based approach might stop with enhanced due diligence procedures and contract clauses. A Baconian approach goes further. It asks whether third parties are actually being onboarded according to policy, whether approvals are properly documented, whether high-risk distributors are subject to enhanced monitoring, whether payments match contractual terms, whether red flags are closed or merely noted, and whether the company can identify trends across geographies, business units, or product lines. That is where compliance becomes operational.

Monitoring Is How a Program Proves Itself

One of the clearest lessons Bacon offers is that observation must be ongoing. In compliance terms, that means monitoring is not an optional add-on. It is how the program proves itself. COSO has long emphasized monitoring as a core element of an effective internal control framework. The same logic applies to compliance more broadly. Monitoring tells a company whether its controls are operating consistently, whether local business practices are drifting from policy expectations, and whether emerging risks are being detected early enough to matter.

Hotline data is a good example. Many organizations report the number of calls received, but that is only the beginning. A Baconian compliance officer looks beneath the surface. Are certain allegations rising in a specific region? Are retaliation claims increasing after a business reorganization? Are reports being substantiated at a lower rate because employees do not understand what should be reported? Are investigation closure times lengthening in a way that undermines confidence in the process? Those are not just operational questions. There are questions about whether the compliance system is learning.

Root Cause Analysis Is Bacon in Action

If there is one area where Bacon’s influence should be explicit, it is root cause analysis. When misconduct happens, the least useful response is to identify the wrongdoer, discipline the individual, and move on. That may satisfy a desire for closure, but it does not satisfy the demands of an effective compliance program.

Bacon would ask a different set of questions. What conditions allowed this to happen? What signals were missed? Were incentives misaligned? Was a manager pressuring a sales team in ways that made policy noncompliance more likely? Did the control exist on paper but fail in operation? Was a prior warning sign identified but not escalated?

Those questions matter because substantive compliance violations are never random. It is often the product of pressure, weak controls, poor communication, bad assumptions, or failures to learn from earlier warning signs. Root cause analysis is the process by which a company examines the conditions that led to a failure and turns that failure into institutional knowledge.

Culture Needs Evidence Too

Compliance professionals often speak about culture, and they should. But here, too, Bacon has a warning for us. Culture cannot be measured only by slogans or tone-at-the-top statements. A company that wants to claim a strong ethical culture should be able to point to supporting evidence.

Do employees raise concerns without fear of retaliation? Are managers evaluated in part on ethical leadership? Do exit interviews reveal pressure points that formal reporting channels miss? Are discipline outcomes consistent across levels of seniority? Does the organization respond to bad news constructively or defensively? These are empirical questions. They require information, not aspiration.

This is where compliance, internal audit, legal, and HR can work together in a mature governance model. Surveys, hotline trends, investigation data, audit findings, and employee feedback all become part of the evidence base. Culture, in this framework, is not soft. It is observable. It can be tested, assessed, and strengthened.

The Compliance Officer as Institutional Scientist

Perhaps Bacon’s greatest gift to the compliance profession is this: he offers a model for what the compliance officer should be. Not merely a policy custodian. Not merely a trainer. Not merely an investigator. The modern compliance leader is, in part, an institutional scientist.

That phrase may sound grand, but it captures something important. The CCO studies how the organization really works. Which incentives shape conduct? Which controls hold under pressure? Where are the blind spots? What do the data show? What must change? In that sense, the compliance function is not external to the business. It is one of the primary ways the business learns about itself.

That is why evidence matters so much. It is the basis for credibility with the board, with regulators, and with employees. It is how a program shows that it is more than a collection of good intentions. Francis Bacon would have understood that immediately.

Five Lessons Learned for the Modern Compliance Professional

First, a compliance program must be judged by evidence, not by appearance. Policies and training matter, but proof of effectiveness matters more.

Second, risk assessments should be treated as working hypotheses that must be tested through monitoring, auditing, and ongoing review.

Third, data is central to the credibility of compliance. Hotline trends, investigation outcomes, audit findings, and control testing demonstrate that a company’s program works in practice.

Fourth, root cause analysis is essential. Misconduct should trigger institutional learning, not merely individual discipline.

Fifth, culture itself must be supported by evidence. Speak-up, non-retaliation, consistency in discipline, and employee trust are all observable markers of program health.

Coming Next: René Descartes and the Discipline of Internal Investigation

If Francis Bacon teaches us how to gather evidence, René Descartes teaches us what to do with it. In Part 2, I will examine how Descartes’ method of disciplined doubt provides a blueprint for internal investigations, allegation triage, and rigorous compliance inquiry. In a world of management narratives, incomplete facts, and pressure to reach quick conclusions, Descartes reminds us that the compliance professional’s first duty is not comfort. It is clear thinking.

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AI Today in 5

AI Today in 5: April 23, 2026, The AI Maga Influencer Edition

Welcome to AI Today in 5, the newest addition to the Compliance Podcast Network. Each day, Tom Fox will bring you 5 stories about AI to start your day. Sit back, enjoy a cup of morning coffee, and listen in to the AI Today In 5. All, from the Compliance Podcast Network. Each day, we consider five stories from the business world, compliance, ethics, risk management, leadership, or general interest about AI.

Top AI stories include:

  1. Agentic AI reshaping bank compliance. (FinTechGlobal)
  2. Compliance First AI for AML. (FinTechGlobal)
  3. Monetizing AI and compliance as a service. (CRN)
  4. Using AI to personalize health care. (Forbes)
  5. The top MAGA influencer is an AI created in India. (NYPost)

For more information on the use of AI in Compliance programs, my new book, Upping Your Game, is available. You can purchase a copy of the book on Amazon.com.

To learn about the intersection of Sherlock Holmes and the modern compliance professional, check out my latest book, The Game is Afoot-What Sherlock Holmes Teaches About Risk, Ethics and Investigations on Amazon.com.

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Blog

The 30-Day Shadow-AI Amnesty: Turning Hidden Risk into Governance

There is a hard truth that every Chief Compliance Officer and compliance professional needs to confront right now: artificial intelligence is already inside your organization, whether it arrived through formal approval channels or not.

Employees are testing tools independently. Business teams are adopting AI-enabled workflows without waiting for a governance committee to approve them. Vendors are embedding AI into products and services faster than many companies can update their policies. Somewhere inside that mix, decisions are being influenced by systems that may not be documented, reviewed, or governed in any meaningful way. That is the world of Shadow-AI.

It is not necessarily malicious. In many cases, it is simply the predictable result of innovation outpacing governance. But from a compliance perspective, that does not make it any less risky. Under the Department of Justice’s Evaluation of Corporate Compliance Programs, the question is not whether management intended to allow uncontrolled use of AI. The question is whether the company can identify emerging risks, implement controls that address them, encourage internal reporting, and demonstrate that the program works in practice.

That is why the 30-day Shadow-AI Amnesty matters. Properly designed, it is not an admission of failure. It is proof of governance. It is a practical mechanism for surfacing hidden risk, reinforcing a speak-up culture, and creating the operational baseline needed to govern AI over the long term.

You Cannot Govern What You Cannot See

The first challenge with Shadow-AI is visibility. Too many organizations still assume that AI risk begins with approved enterprise systems. That assumption is already outdated. The real risk universe is broader. It includes employees using public generative AI tools for drafts or analysis. It includes business units creating internal automations that affect workflows. It includes third-party applications with embedded AI functionality that have not been separately assessed. It includes pilots that started small and quietly became part of day-to-day decision-making.

This is exactly the sort of problem the ECCP is built to address. The DOJ asks whether a company’s risk assessment is dynamic and updated in light of lessons learned and changing business realities. Shadow-AI embodies the changing business reality. If your risk assessment fails to account for hidden AI use, your compliance program is lagging behind the business.

A 30-day amnesty closes that gap by creating a controlled mechanism to identify what is already happening. It allows the company to convert unknown risk into known risk and known risk into governable risk. In other words, it turns hidden risk into a governance advantage.

Why Amnesty Works Better Than Enforcement at the Start

One of the smartest features of a Shadow-AI Amnesty is that it begins with disclosure rather than punishment. If you want employees to report unapproved AI use, you need to give them a credible reason to come forward. If the first signal from compliance is that disclosure will trigger blame, discipline, or reputational harm, employees will remain silent. The result will be exactly the opposite of what the compliance function needs. This is where the amnesty becomes a culture-and-speak-up control.

The ECCP places significant emphasis on culture, internal reporting, and non-retaliation. Prosecutors are instructed to evaluate whether employees feel comfortable raising concerns and whether the company responds appropriately when they do. A well-structured amnesty aligns directly with those expectations because it tells employees that transparency is valued, that reporting is encouraged, and that remediation matters more than finger-pointing.

That does not mean there are no consequences for reckless or prohibited conduct. It means the organization recognizes that the first step toward control is visibility. The safe-harbor period exists to gather information, assess risk, and bring informal AI activity into a formal governance structure. That is not a weakness. That is smart compliance design.

Designing the Amnesty for Participation

The success of a Shadow-AI Amnesty depends heavily on its design. If the process is burdensome, legalistic, or overly technical, participation will be limited. The design principle should be simple: lower the barrier to disclosure while collecting enough information to support triage.

A short intake process is essential. Employees should be able to disclose a tool, workflow, or use case quickly. The company needs basic information: what the tool is, who owns it, where it is used, what data it touches, what decisions it may influence, and whether any controls already exist. This is not the stage for a full investigation. It is the stage for building inventory and context.

That approach is fully consistent with good governance practice. The NIST AI Risk Management Framework emphasizes understanding context, mapping use cases, and establishing governance for the actual use of AI. ISO/IEC 42001 similarly reflects the principle that effective AI management begins with a defined scope, documented processes, and clear responsibility. You cannot apply either framework if you do not know what systems or uses exist in the first place. The amnesty, then, is not a side exercise. It is the front door to a credible AI governance program.

Triage Is Where Governance Becomes Real

Once disclosures start coming in, the company must shift from intake to triage. This is where design and control become critical. Not every disclosed use of AI presents the same level of risk. Some uses may be low-risk productivity aids. Others may influence hiring, investigations, financial reporting, customer-facing communications, or core operational decisions. The compliance function needs a disciplined way to distinguish between them.

A risk-based triage model should ask a few straightforward questions. Does the AI influence a decision that affects employees, customers, or regulated outcomes? Does it involve sensitive or confidential data? Is there human review, or is the output used automatically? Is the use visible externally? Is it part of a business-critical workflow? What controls exist today?

These are compliance questions. They are also ECCP questions because they go directly to risk assessment, resource allocation, and whether controls are tailored to the realities of the business. This is also where culture and control begin to work together. A company that invites disclosure but fails to triage intelligently will lose credibility. Employees need to see that reporting leads to measured, thoughtful governance, not chaos. The point is not to shut everything down. The point is to classify, prioritize, and respond appropriately.

Culture as a Control

One of the most important themes in the modern compliance conversation is that culture is not soft. Culture is a control. That is especially true with Shadow-AI. In many organizations, the first people to know that a workflow has drifted outside approved channels are the employees using it every day. The first people to spot unreviewed prompts, risky data inputs, or overreliance on AI-generated outputs are often not senior executives or formal governance committees. They are line employees, managers, analysts, and business operators.

If those people do not believe they can report what they see without retaliation or embarrassment, then the organization loses one of its most effective early warning systems. A Shadow-AI Amnesty sends a powerful signal. It says the company would rather know than remain in the dark. It says that governance begins with honesty. It says that disclosure is part of doing the right thing.

Under the ECCP, that matters. A culture that encourages internal reporting and constructive remediation is a hallmark of an effective compliance program. In the AI context, it may be one of the few ways to surface emerging risks before they become control failures, regulatory issues, or public problems.

From Amnesty to Operating Model

The amnesty itself is only the beginning. Its true value lies in what follows. Once the company has a baseline inventory of disclosed AI uses, it should not let that information sit in a spreadsheet and die. The next step is to convert the amnesty into a long-term governance operating model.

That means maintaining a living registry of AI use cases. It means embedding disclosure and review into normal business processes. It means defining approval pathways for higher-risk uses. It means establishing ongoing monitoring to detect performance changes, data drift, and control effectiveness. It means updating policies, training, and communications based on what the company has actually learned from the amnesty.

This is where the governance frameworks become especially useful. NIST AI RMF helps organizations move from mapping and understanding AI uses to governing, measuring, and managing them. ISO/IEC 42001 provides the management-system discipline needed to assign responsibility, document controls, review performance, and drive continual improvement.

In other words, the amnesty is not the solution by itself. It is the catalyst that allows a real operating model to emerge.

Proof of Governance Under the ECCP

Why does this matter so much from an enforcement perspective? Because the amnesty produces evidence. If regulators ask how the company identified AI uses, there is a process. If they ask how risks were assessed, there is a methodology for it. If they ask what was done with high-risk cases, there are records of triage and remediation. If they ask what role culture played, there is a concrete speak-up initiative tied to internal reporting and governance design.

This is exactly what the ECCP is looking for. Not slogans. Not a glossy AI principles deck. Evidence that the company identified a risk, created a mechanism to surface it, encouraged reporting, evaluated what it found, and built controls that match the risk. That is why the 30-day Shadow-AI Amnesty is so important. It transforms governance from assertion into proof.

The Practical Bottom Line

The compliance function does not need to wait for a perfect enterprise AI strategy before acting. In fact, waiting may be the biggest mistake. Shadow-AI is already there. The question is whether your organization is prepared to see it, hear about it, and govern it.

A 30-day amnesty is one of the most practical tools available because it combines two things strong compliance programs need: better visibility and a stronger culture. It surfaces risk while reinforcing speak-up. It creates documentation while improving control design. It gives the company a starting point for long-term governance without pretending the problem can be solved in one month.

In the end, that is what good compliance has always done. It does not deny business reality. It creates the structure that allows the business to move forward with integrity, accountability, and confidence.

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Blog

Trust Is Not a Control: The Drop-In AI Audit

There is a hard truth at the center of modern AI governance that every compliance professional needs to confront: trust is not a control. For too long, organizations have approached AI oversight with a familiar but outdated mindset. They collect a vendor certification. They review a policy statement. They ask whether a third party is “aligned” with a recognized framework. Then they move on, assuming the governance box has been checked. In today’s enforcement and risk environment, that approach is no longer good enough.

The Department of Justice has repeatedly made this point in its Evaluation of Corporate Compliance Programs. The DOJ does not ask whether a company has a policy on paper. It asks whether the program is well designed, whether it is applied earnestly and in good faith, and, most importantly, whether it works in practice. That final phrase matters. Works in practice. It is the dividing line between performative governance and effective governance.

That is why every compliance program now needs a drop-in AI audit. It is not simply another diligence exercise. It is a mechanism for proving that governance is real. It is a practical third-party risk tool. And it is one of the clearest ways to operationalize the ECCP in the age of artificial intelligence.

The Problem: Third-Party AI Risk Is Moving Faster Than Oversight

Most companies do not build every AI capability internally. They rely on vendors, service providers, cloud platforms, embedded applications, analytics partners, and other third parties whose tools increasingly shape business processes and compliance outcomes. In many organizations, these third parties now influence investigations, due diligence, monitoring, onboarding, reporting, customer interactions, and internal decision-making. That creates a new class of third-party risk.

The problem is not only whether a vendor has responsible AI language in its contract or whether it can point to a certification. The problem is whether your organization can verify that the relevant controls are functioning as represented in the real-world use case affecting your business. That is where too many compliance programs still fall short.

Under the ECCP, the DOJ asks whether a company’s risk assessment is updated and informed by lessons learned. It asks whether the company has a process for managing risks presented by third parties. It asks whether controls have been tested, whether data is available to compliance personnel, and whether the company can demonstrate continuous improvement. These are not abstract questions. They go directly to how you oversee AI-enabled third parties. If your third-party AI governance begins and ends with a questionnaire and a PDF certification, you do not have evidence of governance. You have evidence of intake.

What a Drop-In Audit Really Does

A drop-in AI audit changes the question from “What does the third party say?” to “What can the third party prove?” That is a profound shift.

The value of the drop-in audit is that it brings compliance discipline directly into third-party AI oversight. Instead of accepting broad claims about safety, control, and alignment, you examine operational evidence. Instead of relying solely on design statements, you test for performance in practice. Instead of treating governance as a one-time approval event, treat it as a repeatable audit process. In that sense, the drop-in audit becomes proof of governance.

It also becomes a far more mature third-party risk tool. You are no longer merely assessing whether a vendor appears sophisticated. You are assessing whether a third party can withstand scrutiny on the questions that matter most: scope, controls, traceability, escalation, and evidence.

And from an ECCP perspective, that is precisely the point. The DOJ has emphasized that compliance programs must move beyond paper design into operational reality. A drop-in audit is one of the few mechanisms that let you do that in a disciplined, documentable way.

From Vendor Oversight to Third-Party Governance

This discipline should not be limited only to classic vendors. The better view is to expand the concept across all third parties that provide, influence, host, or materially shape AI-enabled services. That includes software providers, outsourced service partners, embedded AI functionality in enterprise tools, cloud-based analytics environments, compliance technology vendors, and any external party whose systems affect business-critical decisions or regulated processes.

Risk does not care about the label on the contract. If the third party’s AI affects your organization’s screening, monitoring, investigations, decision support, or disclosures, the compliance risk is real. Your governance process must be equally real. This is why “trust but verify” is no longer just a slogan. It is a design principle for third-party oversight of AI.

The Core Elements of the Drop-In Audit

A strong drop-in audit has three features: sampling, contradiction testing, and escalation.

1. Sampling: Evidence of Operation, Not Merely Design

Sampling is where governance becomes tangible. A company requests specific artifacts tied to actual use cases and actual control operations. This may include scope documents, Statements of Applicability, system documentation, training data summaries, access controls, incident records, runtime logs, or evidence of human review. The point is simple: operational evidence is what matters.

This is where a compliance function moves from hearing about controls to seeing them in action. It is also where internal audit can add real value by testing whether the evidence supports the stated control environment.

2. Contradiction Testing: Where Real Risk Emerges

This is one of the most important and underused concepts in third-party AI oversight. Inconsistencies between claims and reality are where governance failures emerge. If a third party says its certification covers a given service, does the scope document confirm it? If it claims strong incident response, does the record back it up? If it represents strong human oversight, do the runtime traces show meaningful intervention or only theoretical review points?

Contradiction testing is powerful because it goes to credibility. It tests whether the governance narrative matches the operating reality. Under the ECCP, that is exactly the kind of inquiry prosecutors and regulators will care about. It speaks to effectiveness, honesty, and control discipline.

3. Escalation: Governance in Action

Governance without consequences is not governance. A drop-in audit must include clear escalation triggers. Missing evidence, mismatched certification scope, unexplained gaps, unresolved incidents, or inconsistent remediation should not be noted in isolation. They should trigger action.

That action may include enhanced diligence, contractual remediation, independent validation, temporary use restrictions, or deeper audit review. The important point is that the program responds. This is where the drop-in audit becomes operationalizing the ECCP. It demonstrates that the company not only identifies risk but also acts on it.

How the Drop-In Audit Maps to the ECCP

The drop-in audit aligns tightly with the DOJ’s framework for an effective compliance program. Risk assessment is addressed because the audit focuses attention on where AI-enabled third parties create actual operational and control exposure. Policies and procedures are tested because the company does not merely accept them at face value. It assesses whether the stated controls are supported by evidence. Third-party management is strengthened by making oversight continuous, risk-based, and verifiable. Testing and continuous improvement are built into the audit process, which identifies gaps, contradictions, and corrective actions. Investigation and remediation principles are reinforced by documenting, escalating, and using findings to improve the control environment.

Most importantly, the audit answers the ECCP’s central practical question: Does the program work in practice?

How the Drop-In Audit Maps to NIST AI RMF

The NIST AI Risk Management Framework provides a highly useful structure for the drop-in audit, especially through its Govern, Map, Measure, and Manage functions.

  1. Governance is reflected in defined ownership, accountability, and escalation when issues are identified.
  2. A map is reflected in understanding the third party’s actual AI use case, scope, dependencies, and business impact.
  3. The measure is reflected in the use of evidence, runtime observations, contradiction testing, and performance assessment.
  4. Management is reflected in remediation, ongoing oversight, and updates to controls based on audit findings.

In this way, the drop-in audit becomes a practical tool for taking the NIST AI RMF from concept to execution.

How the Drop-In Audit Maps to ISO/IEC 42001

ISO/IEC 42001 adds the management-system discipline that compliance programs need. Its value lies in documented scope, role clarity, control applicability, monitoring, corrective action, and continual improvement. A drop-in audit fits naturally into that structure because it tests whether those elements are visible in operation, not merely stated in documentation.

The Statement of Applicability becomes meaningful when the company verifies that the controls identified there actually correspond to the deployed service. Monitoring becomes meaningful when evidence is examined. Corrective action becomes meaningful when gaps trigger follow-up. Continual improvement becomes meaningful when findings are fed back into governance. That is why the documentation you generate should serve your board, regulators, and internal stakeholders without additional work. Producing evidence that travel is one of the most strategic benefits of this approach.

Why Every Compliance Program Needs This Now

The strategic payoff is straightforward. Strong AI governance is not a drag on innovation. It is what allows innovation to scale with trust. A drop-in audit gives compliance and internal audit a mechanism to test what matters, document their findings, and create evidence that withstands scrutiny. It moves governance from assertion to proof. It transforms third-party diligence into a repeatable, auditable process. It helps ensure that when regulators, boards, or business leaders ask how the company knows its third-party AI governance is working, there is a real answer.

Because, in the end, evidence of governance matters. Not narratives. Not slide decks. Evidence. President Reagan was right in the 1980s, and he is still right today: “Trust but verify.”

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AI Today in 5

AI Today in 5: April 21, 2026, The 7 Questions You Should Ask Edition

Welcome to AI Today in 5, the newest addition to the Compliance Podcast Network. Each day, Tom Fox will bring you 5 stories about AI to start your day. Sit back, enjoy a cup of morning coffee, and listen in to the AI Today In 5. All, from the Compliance Podcast Network. Each day, we consider five stories from the business world, compliance, ethics, risk management, leadership, or general interest about AI.

Top AI stories include:

  1. 7 questions to ask about AI and compliance. (The News Tribune)
  2. Compliance can outsource tools to AI but not judgment. (FinTech Global)
  3. Data Authenticity and Accountability for AI. (CCI)
  4. Do AI chatbots make you stupider? (BBC)
  5. ICU nurses get AI help. (HealthcareItNews)

Interested in attending Compliance Week 2026? Click here for information and Registration. Listeners to this podcast receive a 20% discount on the event. Use the Registration Code TOMFOX20

To learn about the intersection of Sherlock Holmes and the modern compliance professional, check out my latest book, The Game is Afoot-What Sherlock Holmes Teaches About Risk, Ethics and Investigations on Amazon.com.

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Blog

AI Disclosures, Controls, and D&O Coverage: Closing the Governance Gap Around Artificial Intelligence

A new governance gap is emerging around artificial intelligence, and it is one that Chief Compliance Officers, compliance professionals, and boards need to confront now. It sits at the intersection of three areas that too many companies still treat separately: public disclosures, internal controls, and insurance coverage. That siloed approach is no longer sustainable.

As companies speak more confidently about their AI strategies, insurers are becoming more cautious about the risks those strategies create. That tension matters. It signals that the market is beginning to see something many organizations have not yet fully addressed: when a company’s statements about AI outpace its actual governance, the exposure is not merely operational or reputational. It can become a disclosure issue, a board oversight issue, and ultimately a proof-of-governance issue under the Department of Justice’s Evaluation of Corporate Compliance Programs (ECCP).

For the compliance professional, this is not simply an insurance story. It is a compliance integration story. The question is whether the company can align its statements about AI, the controls it has in place, and the protections it believes it has in place if something goes wrong.

The New Governance Gap

Many organizations are eager to describe AI as a source of innovation, efficiency, better decision-making, or competitive advantage. Those messages increasingly appear in earnings calls, investor decks, public filings, marketing materials, and board presentations. Yet the underlying governance structures often remain immature. That disconnect is the governance gap.

It appears when management speaks broadly about responsible AI but has not built a complete inventory of AI use cases. It appears when companies discuss oversight but cannot show testing, documentation, or monitoring. It appears that boards assume that insurance will respond to AI-related claims without understanding how new policy language may narrow coverage.

This is where D&O coverage becomes so important. It is not the center of the story, but it is a revealing signal. If insurers are revisiting policy language and introducing exclusions or limitations tied to AI-related conduct, it suggests the market sees governance risk. In other words, the insurance market is sending a message: AI-related claims are no longer hypothetical, and companies that cannot demonstrate disciplined oversight may find that risk transfer is less available than they assumed.

Why the ECCP Should Be the Primary Lens

The DOJ’s ECCP remains the most useful framework for analyzing this issue because it asks exactly the right questions.

Has the company conducted a risk assessment that accounts for emerging risks? Are policies and procedures aligned with actual business practice? Are controls working in practice? Is there proper oversight, accountability, and continuous improvement? Can the company demonstrate all of this with evidence? Those are compliance questions, but they are also the right AI governance questions.

If a company makes public statements about AI capability, oversight, or reliability, the ECCP lens requires more than aspiration. It requires substantiation. Can the company show who owns the AI risk? Can it demonstrate how models or systems are tested? Can it show escalation procedures when problems arise? Can it document how AI-related decisions are monitored, reviewed, and improved over time?

If the answer is no, then the issue is not simply that the company may have overpromised. The issue is that its compliance program may not be adequately addressing a material emerging risk. That is why CCOs should view AI as a cross-functional challenge requiring integration across legal, compliance, technology, risk, audit, investor relations, and the board.

AI Disclosure Must Be Evidence-Based

One of the most practical steps a compliance function can take is to push for an evidence-based disclosure process around AI. This means that public statements about AI should not be driven solely by enthusiasm, market pressure, or executive optimism. They should be grounded in underlying documentation. If the company says it uses AI responsibly, where is the governance framework? If it claims AI improves decision-making, what testing supports that assertion? If it says it has safeguards, where are the control descriptions, monitoring results, and escalation records?

This is not about suppressing innovation. It is about ensuring that disclosure discipline keeps pace with technological ambition. For boards, this means asking harder questions before approving or relying on public AI narratives. For compliance officers, it means helping management build the evidentiary record that turns broad statements into defensible representations.

Controls Must Catch Up to Strategy

This is where the “how-to” work begins. Compliance professionals should begin by creating a structured inventory of AI use cases across the enterprise. That inventory should identify where AI is being used, what decisions it informs, what data it relies on, who owns it, and what risks it entails.

Once that inventory exists, risk tiering should follow. Not every AI use case carries the same compliance significance. A low-risk productivity tool does not need the same oversight as a system that affects investigations, third-party due diligence, customer interactions, financial reporting, or core operational decisions.

From there, the company can design controls proportionate to risk. High-impact uses of AI should have documented governance, human review where appropriate, testing protocols, escalation triggers, and monitoring requirements. The compliance team should be able to answer a simple question: where are the controls, and how do we know they work? That is the heart of the ECCP inquiry.

Where NIST AI RMF and ISO/IEC 42001 Fit

This is also where the NIST AI Risk Management Framework and ISO/IEC 42001 become highly practical tools. NIST AI RMF helps organizations govern, map, measure, and manage AI risks. For compliance professionals, this provides a disciplined structure for identifying AI use cases, understanding impacts, assessing reliability, and managing response. It is especially useful in linking abstract AI risk to operational decision-making.

ISO/IEC 42001 brings management system discipline to AI governance. It focuses on defined roles, documented processes, control implementation, monitoring, internal review, and continual improvement. That makes it an excellent bridge between policy and execution. Together, these frameworks help operationalize the ECCP. The ECCP tells you what an effective compliance program should be able to demonstrate. NIST AI RMF helps structure the risk analysis. ISO 42001 helps embed those requirements into a repeatable governance process.

For CCOs, the practical lesson is clear: use these frameworks not as academic overlays, but as working tools to build ownership, documentation, testing, and accountability.

Insurance Is a Governance Input

Companies also need to stop treating insurance as an afterthought. D&O coverage should be considered a governance input, not merely a downstream purchase. If policy language is narrowing around AI-related claims, boards and compliance leaders need to understand what that means. What scenarios might raise disclosure-related allegations? Where is ambiguity in coverage? What assumptions has management made about protection that may no longer hold?

Compliance does not need to become an insurance specialist. But it does need to ensure that disclosure, governance, and risk transfer are aligned. If the company is making strong public claims about AI while carrying unexamined governance weaknesses and uncertain coverage, that is precisely the kind of mismatch that can trigger a crisis.

Closing the Gap Before It Becomes a Failure

The larger lesson is straightforward. AI governance is not simply about technology controls. It is about integration. It is about ensuring that what the company says, what it does, and what it can prove all line up. That is why the governance gap matters so much. It is the space where strategy outruns structure, where disclosure outruns evidence, and where confidence outruns control. For boards and compliance professionals, the task is to close that gap before it becomes a failure.

The companies that do this well will not necessarily be the ones moving the fastest. They will be the ones building documented, tested, monitored, and governed AI programs that stand up to regulatory scrutiny, investor pressure, and real-world disruption. That is not bureaucracy. That is the price of sustainable innovation.