Secure your cloud from the harm your
AI agents can do to it.
We don't do prompt security. Limera detects the moment an agent's intent drifts from its actual action — catching adversarial agents and trusted agents that step outside policy, user intent, or expected behavior. Across every cloud, every environment, down to the local agent on a laptop.
Observe-only. Zero inline latency. Connect, don't collect.
- task
- resolve support ticket #4471
- scope
- orders.read · single record
- agent
- support-bot / nhi_44c1
- api
- storage.data.read · 41,802 rows
- cloud
- azure · eastus2
- policy
- allowed by RBAC · wrong for task
Correlating intent and action across your entire agent surface
An AI agent is a privileged identity with no accountability
Agents now hold real cloud permissions — the blast radius of a privileged employee, but no manager, no audit of why, and no off-switch. They cause harm in two ways.
A dangerous action
The agent grants itself Owner, disables logging, makes a database public, or deletes backups. Dangerous no matter who does it — existing tools already alert. We add the missing why: which agent, what task, what reasoning.
A normal-looking action that's wrong for this agent
A support agent asked one order-status question instead reads the entire customer table. It has permission, breaks no rule, trips no signature — so CIEM, DLP and prompt security all stay silent. The only way to catch it is to compare what the agent was asked to do with what it did.
Prompt security guesses intent at the model's mouth and never sees the action. CSPM sees the action and never knows the intent. Limera sits between the agent's reasoning and its hands — the only place you can tell the action didn't match the ask.
Between the agent's reasoning and its hands
We correlate intent — what the agent meant to do — with action — what it actually did. Neither source alone is enough.
Capture intent
Subscribe to the agent's own telemetry — reasoning spans, tasks and tool calls from your existing exports. No agents to install, no code to change.
Capture action
Subscribe to the unbypassable cloud audit stream — every IAM change, data read and API call, attributed to the non-human identity that made it.
Correlate & score
The engine aligns intent to action per session and measures divergence. A permitted action that doesn't match the ask is scored, explained and ranked.
Alert with the why
Findings land in a ledger with full context: agent, session, task, reasoning and the exact action — ready to triage, or later, to block.
Four detectors, one canonical model
Correlation runs inside your tenant. Findings are attributed to a non-human identity and session — observe-only, with no inline latency.
Intent ↔ action divergence
The flagship. Measures how far an observed action strays from the declared task — the class of harm no signature, DLP or prompt filter can see.
Per-agent behavioral baseline
Learns each identity's normal footprint, then flags statistical drift — new scopes, new regions, unusual volume for this specific agent.
High-risk signature rules
Deterministic, MITRE ATT&CK-tagged rules on the audit floor — privilege escalation, log tampering, backup deletion, public exposure. The safety net.
Injection → action causality
Ties a suspected prompt injection to the concrete action it produced, turning a probabilistic verdict into confirmed cause and effect.
Wherever your agents act, we're watching the actions
One control plane for the whole agent surface — no matter which cloud, environment, or framework the agent runs in.
Every cloud
Azure, AWS and GCP control-plane audit — one canonical model across providers.
Every environment
Production, staging, CI, and the local agent running on a developer's laptop.
Every agent
Copilot Studio, Foundry, MCP tool-callers and custom autonomous agents alike.
Adversarial & trusted
Injected, misaligned, or a trusted agent drifting outside policy — all in scope.
Passwordless & in-tenant
Correlation runs in your boundary on managed identity. No secrets, no data leaving.
Observe first, enforce later
Land in alert mode with zero adoption risk; add inline blocking once you trust it.
See what your agents are actually doing.
Explore the Limera console — live intent↔action correlation, an agent inventory, and a findings ledger built to catch the harm nothing else can see.