Sovereign AI control plane · EU

One authorization boundary. Every agent action.

SentinelleIA is a guardian agent platform. It sits in the traffic between your agents and their tools, and between your agents themselves. It authorizes each action as a single indivisible transaction, and writes a chained audit log you can hand to an auditor.

01 · The authorization boundary

Four checks, one transaction

Most controls run beside the agent. A policy is evaluated, and the tool call happens somewhere else, a moment later. That gap is where an agent acts on a permission it no longer holds, and where two concurrent calls both pass a budget check that only one of them should have passed.

SentinelleIA binds four checks into a single transaction, evaluated at the moment of invocation by a supervisor that is distinct from the agents it governs, on a typed inter-agent bus.

1

Tool and agent validation

The tool being invoked is cryptographically the tool that was authorized, and the calling agent holds the delegation it claims.

2

Action budget

A per client ceiling on the number of tool calls a model may trigger within a single response. The counter is local to one request and starts at zero on the next. Past the ceiling the call is denied, the response is closed, and the denial is written to the audit chain.

3

Logged attestation

The decision is written into the chained audit log inside the same transaction, not appended afterwards by a separate process.

4

Trust level

A multi state matrix with hot reloadable thresholds, including a state reserved for human oversight under Article 14.

The four commit together or roll back together. There is no state in which one succeeded and another did not.

Authorize Refuse Escalate Suspend Veto

The action budget is not only a safety control. Companies are starting to hand agents real spending authority: paid API calls, compute, supplier commitments, replenishment. An agent that loops, retries and re-reads drifts on two axes at once, its mandate and its bill, and it drifts for the same reason on both. A ceiling enforced inside the transaction, rather than beside it, bounds both. Without that control, an agent that is merely inefficient and an agent that is out of control look identical until the invoice arrives. At machine speed, that gap is long enough to turn a budget problem into a financial incident.

What we claim, precisely. Tool validation, scopes, audit trails and non-human identity are not new in themselves, and other vendors ship them. What is specific here is that the four are one indivisible transaction rather than four sequential checks, which removes partial authorization and the window of inconsistency between the control and the use. The second specific point is that all of it runs on your infrastructure, with no outbound dependency.
02 · Two planes, intercepted

North-south and east-west, at the same layer

MCP, north-south. Between an agent and its tools. Every tool call is observed and authorized or refused before it executes.

A2A, east-west. Between agents. This is where application firewalls and API gateways are blind, and where one compromised agent propagates in cascade through the ones that trust it.

Both planes run through the same enforcement point, fail closed. Endpoints are pinned on first use, payloads are canonicalised before signature, and the delegation chain is explicit rather than inferred. The audit stream carries 53 event types, of which 25 are MCP and A2A audit events.

Interception is a route. Disabling the route returns the system to its previous behaviour, which is what makes a pilot on a production environment acceptable.

Runtime detectorModeWhat it catches
Indirect injectionMonitoringContent collected or returned by a tool diverts the agent from its mandate.
Dangerous actuationBlockingA side effecting action outside the mandate, a write, a call, a transfer. Blocked before execution.
Alarm maskingMonitoringA manipulation that gets a real incident classified as benign.
Data exfiltrationBlockingA leak through agentic channels: tools, responses, A2A delegation. Cut fail closed.
03 · Proof you can hand over

A chained audit log, and the articles it answers

Every decision and every state transition is written to an audit log chained in SHA-256. Each entry is bound to the one before it, so a record cannot be altered or removed without breaking the chain. That is native evidence for Article 12 on record keeping, produced by the system in the course of doing its job rather than reconstructed afterwards.

On top of the log, a compliance engine carries rules for Articles 9 to 15: risk management, data governance, record keeping, transparency, human oversight, accuracy and cybersecurity. It produces compliance reports, model cards and lineage.

Where we are, exactly. The rules, the chained log, the reports and the model cards exist and run today. The industrialisation of this into end to end automatic evidence generation is the object of our TRL 6 to 8 maturation. The trust matrix already carries the Article 14 state for human oversight, and the oversight server itself is on the roadmap for the second half of 2026.

On the calendar. Regulation (EU) 2026/1744 of 8 July 2026 (Digital Omnibus on AI), in force 27 July 2026, moved the high risk obligations of Annex III to 2 December 2027, and Annex I to 2 August 2028. Articles 9 to 15 are unchanged in substance, only the date of application moved. What applies today: GPAI penalties since 2 August 2026, Article 50, and Article 99(4) on operator obligations. And the next deadline is not 2027: the same regulation adds two prohibited practices, on non-consensual intimate imagery and child sexual abuse material, applicable on 2 December 2026. Anyone selling you an August 2026 deadline is working from an outdated text.

04 · Measured, not asserted

Attack success rate, direct against shielded

Article 9(8) requires testing against preliminarily defined metrics and probabilistic thresholds. That is a measurement, not a questionnaire. So we measure.

You expose an endpoint compatible with the OpenAI API, a test key and a written authorization. On a bench derived from AgentDojo, we run the attack combinations in direct mode, your system on its own, then in shielded mode, the same system under SentinelleIA. You get three numbers, not three adjectives.

0.85Direct attack success rate
17 of 20 landed, range 0.75 to 0.95
0.00Shielded attack success rate
0 of 20 landed, on all three runs
0False positives on the same suite
utility 4 of 5, both modes alike

The third number is the one most vendors leave out. Blocking everything is easy and useless, so the cost in legitimate work refused belongs next to the rest. On this suite that cost is nil: utility is identical in both modes, and the single task that fails also fails without protection. Twenty combinations is a small suite, so this is a result, not a guarantee. A non-zero number would appear here in the same place.

Reference measurement. A series of three runs, 22 August 2026, on the AgentDojo workspace suite with the important_instructions attack. We give the median and the range, because one run is noisy and a single figure hides it. Seventeen vectors were exploitable without protection, all seventeen were neutralised, none remained. These figures move with the gateway build they were measured on, and that build is named in the report you receive. A number published without the version it was measured on is not a measurement.

Mapped to the seven MAESTRO layers. MAESTRO is the Cloud Security Alliance threat model for agentic AI systems, published in July 2025. It decomposes an agentic system into seven layers, from the model itself up to the ecosystem of agents that call each other. Nine agents plus a supervisor operate across all seven. Get the full L1 to L7 mapping →

How a status is read. Exposure is read on direct mode. A vector that succeeds five times out of five against your system on its own stays exploitable, even when nothing lands under protection, unless there is an explicit blocking signal. Protection does not retroactively make a weakness disappear.

05 · Deployment

On your infrastructure, supervisor apart

Container based deployment on your own infrastructure. The supervisor that arbitrates authorizations is separate from the agents it governs and keeps its own audit chain, so the governance of the guardian is itself auditable rather than assumed. No outbound dependency, no call home, no data leaving your perimeter for a decision to be made.

06 · Under the boundary

Nine agents, in production today

The boundary is what governs. The agents are what enforce and observe. They are listed last on purpose, because buying a list of detectors is how you end up with nine tools and no accountable decision.

AgentWhat it does
AI FirewallDeep inspection of inbound and outbound LLM traffic, jailbreak detection, PII masking, rate limiting.Details
GatewayZero-trust gateway for LLM flows, multi provider, injection scoring and immediate blocking.Details
Prompt GuardDetection and masking of personal data before anything leaves the perimeter.Details
Tool ProtectionControl of tool invocation at runtime, against poisoned tool responses and out of mandate calls.Details
LLM SecurityRuntime detection of prompt injection, jailbreaks and exfiltration attempts, with timestamped evidence.Details
VisibilityDiscovery of unauthorized AI usage across the network, and mapping of Shadow AI flows.Details
Supply ChainSBOM for machine learning dependencies, vulnerability scanning and license verification.Details
ResilienceCircuit breaker, isolation of a compromised path and controlled restart.Details
GovernanceAI Act rules, compliance reports, model cards and lineage on top of the chained log.Details

Find out what your agents actually allow

You submit your agentic system. We return a measured exposure report, not another questionnaire. The first level is free.

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