Watching AI agents interact on Moltbook is fascinating. It’s like seeing an ecosystem evolve in fast-forward. Thousands of agents post, debate, coordinate, and adapt with minimal human steering.
For enterprise security leaders, it’s also a preview of what’s coming to security tooling. Not one “smart assistant,” but agent meshes that continuously discover, reconcile, and act across messy enterprise environments.
In the context of Exposure Management, the current human bottleneck is no longer sustainable. Security teams simply cannot manually triage and remediate millions of vulnerabilities. Instead, we require an agentic architecture designed to:
For the enterprise security stack, this demands a fundamental shift. We have spent the last decade building better sensors; now, we must build better actors. The true promise of AI agents in exposure management is autonomous resilience - moving beyond "better decisions" to a verified execution and creating a system that doesn't just report on the attack surface but actively defends it.
But pushing autonomy forward runs straight into the part most security vendors avoid.
Enterprises run on dispersed, contextual, and often tacit human knowledge - the one that never fully makes it into a CMDB record or a policy document.
That’s exactly what breaks naïve automation.
An agent can be technically correct and still be dangerously wrong for the business.
Consider the “technically correct” agent in action:
Everything looks perfect on the dashboard - until you realize what the agent couldn’t see.
That service wasn't just "active." It was a legacy bridge specifically kept alive for a partner integration that runs a batch job only once a month - today. Or perhaps the team knows that this specific service, while redundant on paper, has a "cold start" issue that requires a manual database sync to come back up.
The result - the agent followed the rule, patched the vulnerability, and inadvertently severed a million-dollar revenue pipe.
It could see the code, but not the context.
This happens not because the model is “dumb,” but because the enterprise is not a clean database. Business reality lives in undocumented dependencies, "do not touch" verbal agreements, and fragile workarounds. The most critical context in your organization is often the one thing that isn't written down.
Autonomy works when the decision space is bounded and the blast radius is understood. It fails when risk depends on tacit business context - where the cost of being wrong isn’t a false positive, it’s downtime, churn, or lost revenue.
When inputs are ambiguous, incomplete or socially grounded, the right move isn’t automation - it’s escalation.
An agent’s job isn’t to eliminate ambiguity; it’s to surface it, with context, for human judgment.
The future of exposure management isn’t hands-off autopilot.
It’s machine-speed investigation, autonomous coordination, and bounded execution with human judgment reserved for the moments that actually deserve it.
Greg is a technology leader with over 20 years in development and R&D, specializing in artificial intelligence, big data, and cloud computing. He founded the AI unit in 8200, led cloud and big data R&D, and delivered core data and multilingual AI systems. He holds a BSc from the Technion and an MBA from Tel Aviv University.
