AI agent security from identity to every runtime action
An AI agent security platform for discovering every agent, assigning accountable identity, enforcing least privilege at runtime, containing unsafe actions, and preserving an audit-ready decision trail.
The problem
What this is actually about
An AI agent is not only a model. It is a running workload with an identity, tools, credentials, reachable data and the ability to change another system. Securing only its prompts or outputs leaves the authority behind those actions outside the control boundary.
The failure modes compound as the estate grows: shadow agents have no owner, shared credentials break attribution, standing permissions widen blast radius, untrusted content can steer tool use, and monitoring discovers the incident only after the protected system has already accepted the action.
What the platform does about it
Read from the product architecture rather than written here, so this page cannot promise something no module ships.
What AI agents exist here?
- Connectors across AI platforms, cloud, identity providers, source control, enterprise applications and infrastructure
- Shadow agent discovery from model API traffic, AI keys, MCP configuration, SDK dependencies and automation credentials
- An inventory record per agent: owner, purpose, model, framework, tools, MCP servers, data sources, permissions and secrets
- Ten lifecycle statuses from Discovered through Quarantined and Retired
Who owns each agent, and can it prove what it is?
- A stable agent identity that does not change when the model, infrastructure, credential or deployment does
- Business, technical and security ownership, with an orphaned-agent warning when nobody holds it
- A declared purpose that later becomes part of the authorization decision
- Machine authentication by workload identity, mutual TLS, signed JWT and short-lived tokens rather than permanent keys
- A lifecycle from discovery through review, approval, production, recertification and retirement
What is this agent allowed to reach?
- Role and attribute rules evaluated with agent, user, tenant, data, tool, environment, time, risk and declared purpose in context
- Action-level permissions, so reading a supplier and changing its bank details are separate grants
- Five sensitive action classes from read through draft, reversible execution, human approval and dual approval
- Just-in-time privileges that are issued for one action and expire rather than accumulating
- A credential broker that issues scoped, short-lived secrets on request, so an agent never holds a permanent key
What is it doing right now, and should this action be allowed?
- A gateway between the agent and the tool, API or database it is calling
- A recorded decision per action, carrying agent, user, resource, context, risk, policy version and outcome
- Behavioural baselines, so a hundredfold jump in record reads reads as an anomaly rather than a busy afternoon
- Runaway protection that suspends an agent stuck in a loop before it exhausts cost or capacity
- Human approval for sensitive actions, and two-person approval for critical ones
What exactly happened, and how far did it reach?
- A complete event trail per session: model and version, human user, tools, data, decisions, approvals, secrets and destinations
- An incident timeline to the second, including the actions policy refused
- Session replay of observable requests, tool calls, data access and results
- Blast radius across systems, records, credentials, downstream actions and affected customers
- A kill switch that revokes tokens, closes sessions, disconnects MCP and preserves evidence
What it leaves behind
The artefacts these modules produce. Evidence generated by a decision, rather than assembled for an audit afterwards.
- A first-day discovery report naming agents nobody had registered
- An unregistered-agent alert with provider, data reach and risk
- An owner of record for every production agent
- A purpose declaration an out-of-scope action can be measured against
- A permission inventory showing where an agent exceeds its purpose
- An expiry on every elevated grant
- A credential ledger: which agent held what, against which system, and when it lapsed
- An allow, deny or escalate decision on every governed action
- A suspension when behaviour leaves the baseline
- A defensible timeline for a security review
- Preserved evidence that survives the containment that follows it
See it working
Every claim above has somewhere on this site you can go and check it.
- Watch runtime enforcement work
See an indirect prompt injection reach the action boundary and get refused by deterministic policy.
- Inspect a governed agent estate
See owners, environments, trust states and explainable risk across a worked enterprise inventory.
- Follow the decision trail
Inspect the evidence produced by agent actions, approvals, denials and containment events.
Common questions
- What should an AI agent security platform protect?
- It should cover the whole action chain: discovery, accountable identity, credentials, effective permissions, delegated authority, tool and MCP access, sensitive data, runtime decisions, containment and evidence. A control that sees only prompts or outputs covers one layer of that chain.
- How is an agent firewall different from an LLM guardrail?
- A guardrail classifies model inputs or outputs. An agent firewall evaluates a proposed action against identity, resource, policy and current context, then allows, denies, redacts, rate-limits, sandboxes or routes it for human approval.
- Does the platform replace IAM, SIEM or model security tools?
- No. It consumes identity and security signals from the existing stack, adds the agent-authority-action model those systems do not hold, enforces close to the workload, and exports normalized evidence back to the systems teams already operate.
- Can teams test policies before blocking production traffic?
- Yes. The same policy can run in monitor mode against real or historical decisions, showing what it would have changed before enforcement is enabled.