Control AI before it acts, not after
When AI triggers real customer impact, entitlement changes and budget spend, you need pre-execution authorization and full traceability — a three-layer pyramid that evaluates every request before it runs.
When AI can act, control has to move before execution
Post-hoc review was fine when AI only produced analysis. Once an agent can change an entitlement, spend budget or message a customer, review-after-the-fact is just a record of what already went wrong. Regulators have made the stakes concrete: under the EU AI Act, high-risk AI carries documentation, oversight and transparency obligations, market-surveillance authorities can require withdrawal of non-compliant systems, and penalties reach the higher of EUR 15M or 3% of global annual turnover.
The internal risk is just as real. Analysts find the majority of unauthorized AI activity comes not from attackers but from ordinary policy violations — oversharing, unacceptable use, misguided agent behavior — which is exactly what pre-execution authorization and full traceability are built to stop. Governance is shifting from a policy document to an enforced runtime control, and dedicated AI-governance functions are moving from rare to standard.
- High-risk AI obligations carry enforcement powers from 2026 and penalties up to EUR 15M or 3% of global annual turnover. — EU AI Act — European Commission ↗
- Through 2026, at least 80% of unauthorized AI transactions will stem from internal policy violations — not external attacks. — Gartner AI TRiSM (2025) ↗
- By 2028, 25% of large organizations will have a dedicated AI governance team, up from under 1% in 2023. — Gartner AI TRiSM (2025) ↗
Why this stays unsolved today
Post-hoc review can't stop an autonomous action
Once an agent has acted — changed an entitlement, spent budget, messaged a customer — a downstream review only documents the damage. Control that lives after execution is an incident report, not a safeguard.
Regulatory exposure is now financial
High-risk AI now carries documentation, oversight and transparency duties, market-withdrawal powers and turnover-based fines. Governance you can't evidence at the moment of action becomes direct financial and legal exposure.
EU AI Act penalties for high-risk AI reach the higher of EUR 15M or 3% of global annual turnover. — EU AI Act — European Commission↗Most incidents are internal, not attacks
The dominant failure mode is not a breach — it is oversharing, unacceptable use and misguided agent behavior inside your own policies. Perimeter security does nothing about it; only pre-execution authorization does.
At least 80% of unauthorized AI transactions through 2026 will come from internal policy violations. — Gartner AI TRiSM (2025)↗Two rule sets for humans and AI
When people and agents are governed by separate logic, the gaps between the two become blind spots — an action forbidden for a human slips through because the agent path was never mapped to the same rules.
Pre-execution authorization with full traceability
Every request is evaluated before it runs through a three-layer pyramid — Entity (which object?) then Action (which operation?) then Scope (how far?). Actions fall into four escalating types — read-only analysis, recommendation, controlled writes and high-risk execution — each carrying progressively stricter authorization, so the higher the impact, the more control a request clears before it fires. Agents are constrained to workflow context: they see only what the workflow exposes, use only authorized tools, and generate only permitted content.
Human and AI operators converge on the same governance logic — one rule set, not two — and every action is logged with scenario, node, judgment, rule and outcome, making each decision auditable and revocable. Data residency is configurable by Azure region (US, EU, Asia), and the platform is SOC 2 Type II audited and ISO 27001 certified — so control is enforced at runtime rather than asserted in a policy document.
How it works
The mechanics behind governance & compliance.
Four escalating action types
Read-only analysis, recommendation, controlled writes and high-risk execution carry progressively stricter governance. The higher the potential impact, the more authorization a request must clear before it runs.
Entity → Action → Scope pyramid
Every request is evaluated across three layers — which object (Entity), which operation (Action), and how far it reaches (Scope). Authorization happens before execution, so unpermitted actions never fire.
Full traceability
Human and AI operators share the same governance logic. Every action is logged with the scenario, node, judgment, rule and outcome — making each decision auditable and revocable after the fact.
What good looks like
Directional outcomes grounded in the mechanism above and independent benchmarks — a target to design toward, not a guaranteed result.
Unpermitted actions never fire
Authorization is evaluated before execution through the Entity to Action to Scope pyramid, so an action outside policy is stopped at the gate — risk is prevented, not reviewed after the fact.
Audit-ready by construction
Every action is logged with its rule and outcome, so decisions are reproducible and revocable — the kind of evidence high-risk AI regimes now require providers and deployers to produce on demand.
High-risk AI must meet documentation and oversight duties, enforceable from 2026. — EU AI Act — European Commission↗One governance model for people and agents
Humans and AI share one rule set and one audit trail, closing the internal-violation gap that analysts identify as the dominant source of unauthorized AI activity.
At least 80% of unauthorized AI transactions through 2026 trace to internal policy violations. — Gartner AI TRiSM (2025)↗Frequently asked
Where can our data reside?
Data residency is configurable by Azure region — US, EU or Asia — so you can align deployment with your regulatory and residency requirements. The platform is SOC 2 Type II audited, GDPR compliant and ISO 27001 certified.
How are AI agents prevented from overreaching?
Agents are constrained to workflow context: they see only what the workflow exposes, use only authorized tools, and generate only permitted content. Every request is evaluated before execution through the Entity → Action → Scope pyramid, so authorization is pre-execution, not post-hoc.
Can we audit and reverse what an AI did?
Yes. All AI actions are logged with full traceability of scenario, node, judgment, rule and outcome, and they are auditable and revocable. Human and AI operators run on the same governance logic, so there is a single audit trail across both.
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