The Real Governance Gap

When people talk about the governance gap, they often focus on missing policies. But the deeper issue is usually execution.A policy can look perfect on paper and still fail in practice. That came through strongly in both perspectives during the conversation. The biggest breakdown usually happens when the documented process and the real-world process drift apart. Someone follows “their” way of doing things instead of the approved way. A control point gets skipped. A review step never happens. Nobody notices until there’s a problem.This is one reason annual questionnaires often miss the mark.If you only ask for evidence once a year, you’re getting a snapshot of a moving target. Your environment is changing all the time - new tools, new vendors, new permissions, new workflows. A static questionnaire can’t keep up with that reality. It may satisfy a requirement, but it does not give you operational confidence.Rachel’s point is simple and practical: use automation to understand what is actually happening in your environment, then prioritize from there.That means focusing less on whether a vendor can produce a polished report and more on the fundamentals:

• What access do they have?
• What data do they touch?
• What dependencies do they create?
• What happens if they go down?

If you can answer those questions, you already have a much better view of risk than a stack of checkbox answers will give you.

Start with the three questions that matter most

Rachel boiled it down to a memorable framework:

access, data, and dependencies

Those three questions get you closer to inherent risk than a long questionnaire ever will.

• Access - What systems can this vendor or tool reach?
• Data - What information do they store, process, or expose?
• Dependencies - How badly would your business be affected if they failed?

Those questions help you separate “nice-to-know” details from the controls that truly affect business continuity and security. A vendor may have a great uptime SLA, but if they hold critical data or run a core business process, that SLA alone does not remove the risk.

The AI Threat Landscape Is Changing - Especially Around Access

If there’s one place where AI is going to change third-party risk fastest, it’s access.AI agents are not just passive tools. They can make decisions, change behavior based on their permissions, and interact with systems in ways that feel more like a worker than a script. That matters because every new integration, token, permission set, and workflow expands the attack surface.Historically, a tool had one job and one workflow. Now, an AI-enabled system may be able to do far more than the original design intended. If a token gives it broad access, it may use that access in ways you never expected.That’s where least privilege becomes non-negotiable.The conversation kept returning to a familiar security truth: if you grant access, you need to know exactly what that access allows. With AI agents, that becomes even more important because the agent may be harder to monitor, harder to reason about, and harder to remove once it is embedded into business workflows.This is why security teams need to think of AI agents less like software features and more like employees with permissions.That does not mean the analogy is perfect. AI does not have intent. It is not “good” or “bad” in a moral sense. But it does have goals, and it will pursue those goals as far as its permissions allow. If its parameters are wrong, it may confidently do the wrong thing at scale.

Why dynamic AI agents make old assessment models look outdated

A point-in-time assessment assumes risk is relatively stable. But in an AI-heavy environment, that assumption breaks down quickly.The future of third-party risk is likely to be more dynamic, more interconnected, and more continuous. Instead of one annual questionnaire, you may need ongoing visibility into how vendors and AI agents are behaving in real time or near real time.That doesn’t mean every vendor must expose everything. It does mean that certain baseline controls - encryption, MFA, access boundaries, and other foundational safeguards - should be visible and verifiable. Those should not be secrets.The real shift is from static reviews to living data.When vendors can share trustworthy evidence continuously, you spend less time chasing screenshots and more time dealing with actual risk. That is a huge improvement, especially when your team is overloaded with manual paperwork.

What a Better Model Looks Like: Evidence, Context, and Prioritization

One of the most practical parts of the conversation was the idea that third-party risk should be built on evidence, not guesswork.Rachel described how her team publishes an evidence pack - a verifiable, machine-readable set of controls and outputs that can be used to support audits and trust decisions. The point is not just transparency for its own sake. The point is to make the evidence usable, up to date, and easy to validate.That model matters because it shows what a more modern approach can look like:

• evidence that can be verified
• controls that can be checked automatically
• context that helps humans make decisions
• less time spent on repetitive collection

This is where AI can be truly helpful. Not by replacing the reviewer, but by helping the reviewer understand what matters first.Imagine being able to ask a system to help you identify which vendors need immediate attention, which ones are likely low risk, and which findings should be escalated. That is much more valuable than asking AI to summarize dozens of vendor reports with no context.The difference is not just speed. It’s relevance.A good system doesn’t drown you in more information. It helps you find the right information faster.

The opportunity is not just external - it’s internal too

It’s easy to focus only on vendor risk, but the same problem exists inside your organization.Teams are adopting AI tools and workflows without always understanding what data is being used, where it is going, or what controls are in place. That means third-party risk and internal security are converging. The same questions apply whether the tool is external or embedded in your own stack.What data does it touch? What access does it have? What business process depends on it?If you don’t know the answers, you don’t really know the risk.And that’s where AI can actually help, if it’s fed real facts instead of assumptions. It can assist with mapping tools, identifying patterns, and surfacing dependencies. But if the underlying data is junk, the output will be junk too.That’s the part many teams miss. AI does not create truth. It amplifies the quality of the inputs you give it.

Check out our podcast: https://thirdpartythreathunters.buzzsprout.com

####