Interview With Michael J Bannach, Founder & President Of Stealth Technology Group On AI Governance Blind Spots Putting Bosses’ Jobs At Risk

What are the AI governance blind spots putting jobs on the line, and how do you fix them?

The biggest AI governance blind spot is unclear accountability. Many organisations have ethical AI policies, but few assign real ownership for AI risk in practice. That leaves executives exposed to systems that may be biased, insecure, or non-compliant without their knowledge. The fix is to make governance operational: name a senior owner for AI risk, map where AI is used, apply consistent risk assessments, and require ongoing monitoring and reporting. When accountability is explicit, boards can see who is responsible for decisions and outcomes before regulators do.

So who actually owns AI risk in a company, and what happens when no one does?

Ownership of AI risk should sit with a senior executive – often a Chief Risk Officer, Chief Information Security Officer, or a specially designated AI risk lead – supported by cross-functional teams from legal, IT, product, and compliance. When no one owns AI risk, accountability becomes diffuse. Decisions fall through the cracks, regulatory requirements may be missed, and operational failures can escalate without intervention. For executives, this means that if something goes wrong, responsibility may default to the C-suite, creating personal liability and reputational damage even if they weren’t directly involved in AI decisions.

Can you share examples of decisions or failures in AI governance that could have serious consequences for executives personally?

Executives could face serious consequences in cases such as deploying AI systems that make discriminatory decisions in hiring or lending, failing to secure sensitive customer data processed by AI, or releasing models that operate without sufficient testing or transparency. Even a seemingly minor oversight – like failing to document how a high-stakes AI model was trained or monitored – can expose the company to regulatory fines, legal action, and public backlash. In some jurisdictions, regulators are explicitly looking at board accountability for AI governance, meaning executives could be personally questioned or sanctioned if failures are traced back to a lack of oversight.

Why do so many companies think their AI policies are enough – and why could that false sense of security threaten careers?

Many organisations believe a written AI policy satisfies regulatory or governance requirements, but a policy is just a statement of intent. It doesn’t ensure processes are followed, risks are assessed, or decisions are auditable. Executives who rely solely on policies risk being blindsided when regulators or boards ask for evidence of operational governance. A false sense of security can leave leadership unprepared to answer questions about accountability, compliance checks, or risk mitigation measures, putting careers at risk when the gap is exposed publicly or in regulatory reviews.
 

How are boards starting to ask about AI governance, and why is it suddenly a top-of-mind question for executives?

Boards are increasingly aware that AI systems are strategic assets that carry legal, ethical, and reputational risks. They are now asking executives not just whether AI is being used responsibly, but who is accountable for AI risk, how risk is assessed, and whether processes are in place to catch failures before they escalate. This focus has intensified as regulators begin to draft AI-specific compliance frameworks and high-profile AI incidents make headlines. For executives, AI governance is a board-level concern that can influence decision-making and oversight scrutiny.

If an executive hasn’t addressed AI governance, what questions might a board or regulator ask that could expose them?

Boards and regulators are likely to ask questions such as: Who is responsible for AI risk in this organisation? How do you ensure your AI systems are compliant with emerging regulations? What procedures exist to detect bias, security vulnerabilities, or unintended consequences? Can you demonstrate auditable trails for critical AI decisions? If executives cannot answer these questions with evidence of operational governance, they risk being seen as negligent, which could lead to reputational damage, regulatory penalties, or personal accountability in jurisdictions where executives are expected to oversee AI risk.

What’s the single most urgent action a C-suite leader should take today to avoid being caught out on AI governance?

The most urgent action is to assign clear ownership of AI risk at the executive level and ensure that this ownership comes with defined responsibilities, processes, and reporting mechanisms. This includes mapping all AI initiatives, implementing structured risk assessments, and establishing audit trails for AI decision-making. Once responsibility is explicit and governance processes are operational, executives can demonstrate accountability to boards and regulators, reducing both organisational and personal risk.