Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

There is an old saying: Trust, but verify. For Third-Party Risk Management auditors in regulated financial institutions, that principle has never been more relevant. Vendor questionnaires, SOC 2 reports, and annual reassessments are no longer enough. Regulators are moving beyond paper-based oversight and toward operational proof. The new expectation is clear: Show where customer data is actually flowing. Prove that you control it.

This is why platforms like ServiceNow AI Control Tower are showing up in governance roadmaps. Control Tower helps organizations standardize how AI systems are requested, reviewed, cataloged, and managed across their lifecycle. It can bring order to chaos. But there’s a second, equally important reality: the strongest governance workflow in the world can’t govern what it can’t see.

Artificial intelligence has stormed the enterprise world, and it's not slowing down anytime soon. With thousands of AI-powered applications, from large language models (LLMs) to productivity-boosting copilots, employees are tapping into AI to work smarter and faster. But here’s the rub: while AI can supercharge productivity, it also brings along a Pandora’s box of risks.

The modern automation stack makes it almost trivial to connect enterprise tools: By combining these, you can create an “AI agent” that reads a message in Slack, uses an LLM to classify or summarize it, and then creates a new ticket in YouTrack—all in minutes.

An AI Bill of Materials (AIBOM) addresses this gap. It is a concise, living profile for every AI capability an organization can invoke—models, agents, SaaS features, plug‑ins, and APIs. Kept in a machine‑readable format, it serves as a practical record that can inform runtime decisions in a control plane. An AIBOM summarizes five things about each AI capability: who provides it, what it can do, what data it sees, where it runs, and how it should be treated.

The challenge isn’t just that AI agents are new. It’s that they blur traditional boundaries of data control, creating hidden sub-processors and uncontrolled data flows. For CISOs, compliance officers, and security leaders, this presents a fundamental governance problem: if you don’t know which AI services are touching your data, you cannot prove compliance.