Published 1 hour ago

AI Governance for Enterprises: How Qualys TotalAI Secures GenAI, LLMs, and Agents

Most enterprises are running AI faster than they can secure it. Chatbots, copilots, autonomous agents, and LLM-powered workflows are already embedded in production environments—often without a clear inventory, a security review, or any way to prove controls are working.

That gap is exactly where things go wrong.

AI/ML-related breaches already affect a meaningful share of organizations, and prompt-injection attempts have surged dramatically year over year. Shadow AI—employees using unsanctioned tools outside IT’s visibility—is adding breach costs that can run into the hundreds of thousands of dollars per incident. And regulators are no longer waiting: the EU AI Act is fully enforced, NIST’s AI Risk Management Framework is becoming the de facto U.S. baseline, and state-level laws are following close behind.

The problem isn’t that enterprises lack policies. It’s that they lack proof.

7 mins read

9 sections

Key Highlights

Why AI governance fails without continuous, verifiable technical evidence
How Qualys TotalAI links discovery, testing, and remediation into one AI risk workflow
Turning AI telemetry into audit-ready proof for regulators, auditors, and boards

The Practical Takeaway

The AI governance conversation has shifted. It’s no longer enough to have a policy that says “we test our models” or “we monitor AI usage.” Regulators, auditors, and boards increasingly want to see the evidence: the inventory, the test results, the remediation records, the runtime logs.

That’s a fundamentally different operational requirement than what most security teams are set up to deliver today.

Qualys TotalAI is positioned as one answer to that gap—connecting discovery, behavioral testing, prioritized remediation, and continuous monitoring into a workflow that produces audit-ready evidence across the full AI lifecycle. Whether it’s the right fit depends on your existing stack, your regulatory exposure, and the scale of your AI deployment.

But the underlying problem it’s solving—the evidence gap in AI governance—is one that every enterprise running GenAI at scale will need to address, one way or another.