Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A CISO can run a genuinely strong security program and still lose the room the moment they start presenting it to the board. They lead with what they know best: threats, vulnerabilities, mean time to detect, a heat map of likelihood and impact. It’s accurate, but it doesn’t tell the board what any of it means for the business.

Most GRC platforms claim to use AI. That makes it harder, not easier, to judge the near-identical claims in every demo. Leaders need to know the standards to hold these claims to, what can actually be verified, and how to tell two product pitches apart before letting one of them touch critical controls. It helps to split what you are looking at into two groups: demo-grade AI and enterprise-grade AI. Nearly every GRC vendor now describes itself as the second.

A buyer’s checklist for the IRM gaps a ServiceNow program leaves open. Many teams deploy IRM, watch the assessments come back clean quarter after quarter, and reasonably conclude they are covered. Months later, the greatest risk turns out to have been outside the sample. It may have changed the week after the review, or lived in a control type nobody tested, or lacked context or prioritization to see its importance.

GRC transformation is the organizational change from running governance, risk, and compliance as periodic, check-the-box paperwork to running it as continuous, AI-native cyber risk assurance measured in business outcomes. It is not a tooling upgrade. It is a change in what you are asked to prove. The old question was whether the work got done by audit time. The new question is whether risk is understood and the controls meant to manage it are working right now.

“Friends don’t send friends security questionnaires. “If you hang around me long enough, you will hear me say it. It gets a laugh, but the point underneath it is serious. Are security questionnaires enough to manage third-party risk? No. A questionnaire tells you what a vendor is willing to claim on a given day. It does not tell you whether the control behind that claim is working. Those are two very different things, and most third-party risk programs are still built on the first one.

Enterprise security teams need to secure a risk surface that is constantly changing. However, the tools in their stack were built to check only a fraction of that risk. For confirmation, they rely on static snapshots and annual attestations. I now see this as the defining problem in GRC. When 451 Research (S&P Global) initiated coverage of TrustCloud in this space, they described a clear and growing divide.

Enterprises settled the question:“is my software actually working” about a decade ago. Not by hiring more people to read logs, but by instrumenting the data plane once and letting anyone query it. Observability became infrastructure, and the people who used to read logs went and solved harder problems. GRC has never had that moment. We still read the logs, opine, and complete the attestation. And then it stops. The demo proved the concept and became the ceiling.

Continuous control monitoring may sound like a program you have to rebuild your whole GRC function to reach. It isn’t. It’s a phased build that integrates with the systems you already run, and a focused team can have continuous monitoring live across its priority controls in about a month.

If your board asked today how you are governing AI, how would you respond? Not just the policy you wrote, but what is actually happening across the business. Could you answer with evidence? Many CISOs cannot answer with certainty. AI has entered the business faster than anyone could write policy for it, and securing it across all areas now seems to be the CISO’s responsibility.

Every CISO knows they need to do more with less. Fewer analysts, tighter budgets, more obligations. Matthew Martin has led security through all of it in two very different worlds: 20 years in financial services, and now in higher education at Western Carolina University. After two decades with enterprise budgets and every tool available, Matt made a deliberate choice to take on higher ed with different constraints and a decentralized structure.