Prevent Unauthorized AI Decisions
Before They Happen
Runtime governance that enforces enterprise policy, required approvals, and separation of duties — while producing audit-ready evidence for every AI action.
How Tracefort Works
Illustrative Decision Flow
What Happens Without Runtime Governance
These aren't theoretical risks. They're operational failures enterprises face today when AI agents operate without centralized enforcement.
Unsafe Autonomous Execution
Probabilistic models bypass safety checks without runtime interception.
Untraceable Decisions
No way to reconstruct what policy governed a specific AI action.
Embedded Governance
Governance in app code creates policy drift and fragmented enforcement.
Shadow-Agent Sprawl
Unmanaged agents operating without centralized policy authority.
Real Failure Modes Tracefort Is Built to Contain
Concrete operational scenarios where traditional AI governance breaks down — and how runtime enforcement contains them.
Unauthorized ERP Transaction
An AI procurement agent creates a purchase order in SAP without centralized approval. Embedded workflow checks drift over time.
Prompt Injection to Tool Execution
An internal copilot is manipulated into invoking CRM write operations. Gateway-level filters miss the semantic intent.
Audit Reconstruction Failure
Six months after an AI-assisted decision, a regulated enterprise cannot reconstruct the exact policy or approval state.
Shadow-Agent Sprawl
Teams deploy autonomous agents across departments without centralized authority or consistent policy enforcement.
What Tracefort Gives You
Prevent Unauthorized Actions
Every AI request is evaluated against centralized policy before execution. Unauthorized actions are blocked — not detected after.
Prove Governance Was Enforced
Immutable decision records with cryptographic integrity link every AI action to the policy, identity, and approval that governed it.
Enforce Required Approvals
High-risk AI actions are paused for human review. Approvals are stateful, context-preserving, and linked to enforcement.
The Tracefort 3-Layer Model
Governance authority decoupled from execution logic. Every AI request flows through three layers before reaching downstream systems.
Other tools explain what happened. Tracefort prevents what shouldn't.
Built for Production, Not Experiments
| Feature | Traditional AI Tools | Tracefort |
|---|---|---|
| Enforcement | Post-hoc monitoring & alerts | Real-time deterministic enforcement |
| Governance | Embedded in application logic & prompts | Authoritative centralized control plane |
| Audit | Fragmented application logs | Immutable decision lineage & evidence |
| Reliability | Probabilistic 'Best Effort' | Deterministic Execution Control |
| Execution | Unsafe autonomous action | Governed runtime interception |
| Failure Mode | Fail-open (undefined behavior on error) | Fail-closed (always DENY when uncertain) |
| Cost Control | Post-hoc spend alerts | Pre-execution budget enforcement with token-level attribution |
Enterprise Use Cases
How the world's most regulated industries use Tracefort to scale AI safely.
AI Governance for Regulated Industries
Enforce strict data sovereignty and compliance policies across all AI-driven workflows in finance and healthcare.
Read Solution BriefLLM Access Control
Granular, identity-aware control over which users and systems can access specific models and data sources.
Read Solution BriefAgent Workflow Oversight
Deterministic guardrails for autonomous agents, ensuring they never exceed their authorized operational bounds.
Read Solution BriefCompliance Automation
Automatically generate audit-ready documentation for every AI decision, reducing the burden on compliance teams.
Read Solution BriefReady to prove your AI governance actually works?
Tracefort provides the runtime evidence that governance is enforced — not just documented.