See, understand and govern every AI agent in your enterprise.
From discovery to runtime enforcement, Rulebound maps each agent's behavior, its non-human identity, the threats it introduces and its blast radius across cloud, Kubernetes and endpoints — and stops the actions that cross the line.
Illustrative. A single agent run, reconstructed and enforced in real time.
An AI agent is not a model you prompt. It is an autonomous actor that calls tools, moves data and holds standing access — and it behaves differently every time it runs. That is a new class of insider, and the controls you already own cannot see it.
The same agent takes a different path on every run. You cannot secure it with a static rule written for a deterministic application.
Product and data teams spin up agents on cloud platforms, internal frameworks and no-code tools. Most never reach a security review.
Every agent holds keys, roles and tokens that persist between runs — over-privileged by default, and outside every human access review.
IAM shows the grant, not the use. Cloud logs show the API call, not the intent. Endpoint tools watch the host, not the agent. None of them see what the agent actually did.
Rulebound closes the gap between what an agent was granted and what it actually does — continuously, and in real time.
Every agent finding lands on the security frameworks your team and your board already use.
So your agents can act across your business — without acting against it.
We derive an agent's true privilege from what it does at runtime — not from static configuration or after-the-fact cloud logs.
We stop rogue agent actions inline, including inside Kubernetes — not just flag them on a dashboard the next morning.
Coverage across the full agentic threat landscape, mapped to recognized standards — not a handful of demo attacks.
The same agent is traced from discovery, to its runtime behavior, to its non-human identity, to its framework mappings — one graph, not five disconnected tools.
Request access and we will map the AI agents running in your environment, and show you the ones acting outside their limits.
Any autonomous or semi-autonomous system that uses a model to decide and act — calling tools, querying data, or triggering workflows — rather than only returning text. That includes agents built on cloud platforms, internal frameworks, MCP tool servers, and copilots wired into your systems.
IAM shows what an agent was granted; cloud logs show individual API calls after the fact. Neither shows the agent's intent or its behavioral chain. Rulebound reconstructs what the agent actually did, derives the privilege it truly uses, and can stop an action in real time — before it completes.
Yes. Rulebound discovers agents across Kubernetes, managed cloud model and agent services, and endpoints — including shadow agents that never went through a security review.
Block. Enforcement runs inline at the tool and model layers and inside your Kubernetes cluster, so a risky call is stopped as it happens — not surfaced on a dashboard afterward. Monitoring-only mode is available where you want to observe first.
By assessing behavior, not static rules. Every run is scored against the agentic threat taxonomy and against the agent's own learned baseline, so a novel path that crosses a policy or drifts from normal is caught even though you never wrote a rule for it.
OWASP for Agentics and LLMs, MITRE ATLAS, NIST AI RMF and MAESTRO. Regulatory evidence for the data your agents process (DPDP, GDPR) is produced by our separate AI Governance & Compliance solution, so security and privacy stay cleanly separated.