Endeavor for CISOs and risk leaders
Endeavor puts AI agents inside your security perimeter rather than beside it. Redaction before data leaves, permissions that follow the human, forensic logs at the reasoning level, and containment you can exercise on one agent without touching the others.
Agents break the assumptions your controls were built on
Identity management assumes a human actor. Data-loss controls assume a file, not a prompt. Incident response assumes you can reconstruct what happened from system logs that never recorded why a model chose an action.
Meanwhile every SaaS platform has quietly shipped an assistant, and staff are pasting internal data into consumer tools. The exposure is already there; what is missing is a place to see and govern it.
Where the risk actually sits
Prompt injection
Content fetched from the web or a document can carry hidden instructions. Endeavor treats all fetched content as untrusted, constrains tools to declared parameterised operations, and logs every call so an attempt is visible.
Data leakage through prompts
The most common AI incident is not a breach but a paste. Entity extraction redacts sensitive values before a prompt is sent, and discard lists and pattern rules catch what classification alone misses.
Over-privileged agents
An agent on a god-mode service account bypasses Zero Trust entirely. Endeavor scopes each call to the initiating user's clearance.
Unattributable activity
Shadow AI is invisible by definition. A live agent registry replaces guesswork with an inventory of every agent, its models, and its data permissions.
What you get for incident response
| Capability | What it gives an investigator |
|---|---|
| Reasoning log | The chain of thought behind a specific output |
| Source attribution | Exactly which records the agent read |
| Instruction versioning | An immutable record of every change to an agent's prompt |
| Granular containment | Pause or roll back one agent, leaving the rest running |
| User attribution | Every interaction tied to an identity, non-repudiable |
| Centralised revocation | Joiner/mover/leaver handled in one place |
Deployment that satisfies a security review
- Runs in a private Kubernetes namespace inside your own VPC.
- No direct public exposure; egress restricted to endpoints you approve.
- Bring your own model keys, held in your secret manager.
- Models can be hosted locally so no data reaches a third-party provider.
- Encryption in transit and at rest, with source-level access logging.
Frequently asked questions
What new attack surface do AI agents create?
Models accept ambiguous input and try to produce a useful answer, so any content an agent reads is a potential instruction. Hidden text in a web page or document can attempt to make an agent leak data or take a harmful action. Guardrails are part of the answer; constrained tools and complete logging are the rest.
How do we stop sensitive data reaching a third-party model?
Entity extraction identifies and redacts personally identifiable information before a prompt leaves your environment, and models can be hosted locally so no data reaches an external provider at all.
Can we isolate a single misbehaving agent?
Yes. Agents can be paused or rolled back individually without taking down the rest of the estate, and the reasoning log supplies the forensic trail for the review.
How do agent permissions work?
An agent inherits the clearance of the person who invoked it rather than running on a shared service account, so it cannot reach a record that person could not open themselves.
What happens to agents when someone leaves?
Agent ownership and credentials are centralised, so instructions and keys created by a departing user are revoked in one place rather than left running unattributed.