AI agent security
Your AI agents can act.
Frame them to move faster.
AI agents no longer just answer questions: they call APIs, read data, trigger workflows and act on someone's behalf.
The question is not whether to allow them, but how far, in which context, and with which evidence.
We secure the agent lifecycle and the real-time authorization decision, so your business teams can ship their use cases without waiting.
- Framing in weeks, not quarters.
- Works with your existing IAM, IGA and Access Management platforms.
Our compass
No sensitive agent action without identity, context, policy, decision and trace.
This simple rule gives business, product and security teams a shared reference: trade-offs become explicit instead of turning into a veto.
An AI agent acts under mandate, never on its own
An agent is an actor in your information system: it has an identity, a scope, a sponsor and a lifespan.
Treating it as an anonymous technical account means losing track of what is done, by whom, and why.
Its own identity
Every agent gets a distinct identity, tied to an accountable team and a documented use case.
An explicit mandate
The agent acts for a user or a business process, and that mandate is carried all the way to the access decision.
Readable boundaries
Data scope, allowed actions, execution context and duration are written down, versioned and auditable.
Two complementary capabilities
Securing an agent means controlling its lifecycle and deciding, at the moment of the action, whether it should be allowed.
Protect the agent lifecycle
What the agent is, what it carries, what it exposes
- Inventory of agents, their triggers and their dependencies
- Dedicated, short-lived identities and secrets, with no shared credentials
- Control over the tools and connectors an agent can reach
- Segregation of execution environments and accessible data
- Action logging and detection of usage drift
Decide authorization in real time
What the agent is allowed to do, here and now
- Externalised fine-grained policies, shared by humans and agents
- Contextual decisions: mandate, data sensitivity, channel, time, risk
- Control at API and resource level, not only at application level
- Versioned, testable and replayable policies
- Decision evidence usable for audit and investigation
One keeps agents from becoming a blind spot. The other turns your business rules into decisions enforced on every call.
The chain behind an agent action
A safe agent action always follows the same sequence. Every missing link is a blind spot.
- Mandate
- Identity
- Context
- Policy
- Decision
- Action
- Trace
We instrument that chain end to end, then make it observable by the teams running the agents.
How we work
A short progression, designed to deliver one governed use case rather than another architecture document.
- 1
Framing
We start from business use cases already running or planned, and from the expected value.
- Agent inventory
- Data and APIs involved
- Sensitivity level
- Named owners
- 2
Authorization model
We translate business rules into readable policies that apply to humans and agents alike.
- Available attributes
- ABAC / PBAC rules
- Edge cases
- Test suites
- 3
Implementation
We connect the authorization decision to real call points and secure the agent lifecycle.
- Decision points
- Identities and secrets
- Logging
- Execution guardrails
- 4
Operations
We equip your teams so policies can evolve without reopening a project for every new use case.
- Decision review
- Policy evolution
- Indicators
- Skills transfer
The first use case becomes the template: the next ones reuse the same policy model and the same evidence chain.
Use cases you can finally ship
The goal is not to block agents, but to make acceptable the uses that would otherwise stay on hold.
Internal assistants
- Document access based on the requester's real entitlements
- Answers limited to the user's business scope
- Reviewable history when an answer is challenged
Operational agents
- Write actions subject to an explicit decision
- Temporary elevation instead of standing privileges
- Clear separation between read, write and administration
Customer-facing agents
- Control of the data exposed along each journey
- Decisions accounting for channel and authentication level
- Traceability aligned with compliance requirements
System-to-system agents
- Authorization at API and resource level
- Mandate propagated from call to call
- Shared policies across several applications
Security meets business
We do not arrive with a list of prohibitions: we arrive with a frame that lets you say yes more often, faster, and with evidence.
Business teams keep ownership of their use cases. Security gets the readability and traceability it asks for.
Start from value
Every policy maps to an identified use case and an owned business benefit.
Write down what is allowed
Explicit, testable and versioned rules replace informal case-by-case arbitration.
Make teams autonomous
We transfer the skills so policy changes never depend on us.
What you get
Deliverables you can act on, not another report.
Frame
- Map of agents and their mandates
- Authorization model covering humans and agents
- Usage rules validated with the business
Implementation
- Policies implemented and tested
- Decision points wired into real calls
- Dedicated identities and secrets for agents
Operations
- Decision log usable for audit
- Indicators tracking agent usage
- Review and evolution procedures
What changes
Faster delivery
Use cases reach production within a frame defined up front.
Explainable decisions
Every approval or refusal relies on a readable policy and a trace.
Fewer dormant rights
Agents get contextual access instead of standing privileges.
Sustainable compliance
Evidence is produced by normal operations, not reconstructed afterwards.
Fine-grained authorization with Axiomatics
For real-time authorization decisions we rely in particular on Axiomatics, a fine-grained dynamic authorization platform.
It externalises access policies and enforces them consistently across users, applications and agents.
- Centralised ABAC and PBAC policies
- Contextual decisions at call time
- Protection for APIs and distributed architectures
- Traceability of access decisions
Frequently asked questions
Do we need to replace our existing IAM platform?
No. This offer sits on top of your IAM, IGA and Access Management building blocks: it reuses your identities and attributes to carry the decision through to the agent action.
How do we start without blocking teams already experimenting?
We start from one real use case, often already running. The frame is built around it, then reused for the next ones.
How is this different from managing entitlements?
Entitlement management grants permissions in advance. Fine-grained authorization decides at action time, factoring in mandate, context and data sensitivity.
How do we prove what an agent did?
Every decision is logged with its context and the policy applied, so an action can be replayed during an audit or an investigation.
How long for a first scope?
A few weeks to frame and instrument a first use case, depending on team availability and access to environments.
Let's talk about your AI agents
Describe a use case you are working on: we will show you how to frame it without slowing it down.