AI Governance
Govern AI decisions with the rigor they demand
Register, monitor, and govern every AI model in your enterprise. Detect bias and drift, enforce human-in-the-loop policies, and maintain the audit trails regulators require — from EU AI Act to NYC Local Law 144.
Challenges We Solve
Governance gaps that structured controls and audit trails eliminate.
Shadow AI Sprawl
Unsanctioned AI tools proliferate across teams with no inventory or risk scoring. Shadow AI Discovery inventories every tool and scores risk per team, while Agent Authority Boundary ensures agents stay inside their delegated envelope.
Prompt Injection & Jailbreaks
Deployed AI surfaces are vulnerable to adversarial inputs that bypass safety controls. Prompt Injection Exposure quantifies your attack surface, and LLM Jailbreak Resilience stress-tests defenses against evolving jailbreak corpora.
Missing Accountability & Provenance
When AI produces a bad output, tracing it back to the responsible prompt, model version, and human edit is impossible. AI Output Attribution Chain and Model Provenance Attestation close this gap.
Rubber-Stamp Oversight
Human-in-the-loop checkpoints exist on paper but don't change outcomes in practice. Human-in-the-Loop Efficacy measures whether HITL reviews actually alter decisions or just add latency.
Use Cases
Real governance scenarios powered by DecisionLedger.
Runs Shadow AI Discovery across all teams, uncovering 23 unsanctioned AI tools. Scores each with Agent Authority Boundary, then uses Autonomy Graduation Readiness to determine which agents are safe for autonomous operation.
Full AI inventory with risk tiers — 8 tools flagged for remediation, 4 graduated to autonomous
Uses AI Evaluator Calibration to detect a 12% drift in their LLM-as-judge scoring. Runs Human-in-the-Loop Efficacy to prove HITL reviews are changing outcomes. Generates EU AI Act evidence from Model Provenance Attestation and AI Output Attribution Chain.
Calibration drift caught before regulatory review — compliance evidence generated automatically
After a prompt injection incident, runs Prompt Injection Exposure across all AI surfaces and LLM Jailbreak Resilience stress tests. Uses Tool Call Chain Risk to identify agent workflows with unsafe rollback paths, then deploys the kill switch on high-risk agents.
Attack surface quantified, 3 vulnerable surfaces hardened, incident contained with full audit trail
Measurable Impact
Based on platform benchmarks across early adopters.
Built-in AI Governance Controls
Not just models about AI risk — actual enforcement tools that register, monitor, and control every AI agent and model in your organization.
Agent Registry
Register every AI agent with per-agent permissions, activity monitoring, and instant suspension. Know exactly what AI is doing across your organization.
Kill Switch
Circuit breaker for any AI model or agent. One-click disable across your entire tenant with automatic cool-down re-enable when you're ready.
Shadow Mode
Test new models in production without affecting live decisions or audit logs. Validate AI outputs side-by-side before going live.
Bias Auditing
Statistical bias detection across protected classes with a dedicated bias dashboard. Surface disparate impact before it becomes a compliance finding.
SHAP Explainability
Every prediction comes with SHAP waterfall plots showing feature importance and input-to-output transparency. No more black-box AI decisions.
Policy Guardrails
Define enforceable constraints with JSONLogic rules. Auto-flag, block, or escalate violations with full override tracking and rationale logging.
Drift Detection
Monitor policy and guardrail effectiveness over time with automated alerts when controls go stale. Detect when regulatory or policy changes invalidate existing controls.
Compliance Badges
Pre-mapped controls for EU AI Act, NYC Local Law 144, SOX 302/404, and DOL Fiduciary Rule. Generate compliance evidence automatically from your governance activity.
Connects With
Featured Models
Pre-built decision models ready to run with your data.
Algorithmic Impact Assessment
Formal AIA as quantitative decision model with statistical bias analysis, EU AI Act article scoring, and remediation roadmap
Agent Authority Boundary
Scores whether an AI agent's proposed action falls inside its delegated authority envelope.
Prompt Injection Exposure
Quantifies organizational exposure to prompt injection across deployed AI surfaces.
AI Evaluator Calibration
Detects drift in LLM-as-judge scoring relative to human ground truth.
Model Provenance Attestation
Verifies model lineage, training data attestation, and supply-chain trust.
Agentic Workflow Replay Risk
Scores reproducibility risk of agent decisions when re-executed against changed external state.
RAG Grounding Quality
Measures retrieval grounding strength, hallucination probability, and citation faithfulness.
AI Cost Attribution Anomaly
Detects anomalous per-team or per-feature LLM spend.
Shadow AI Discovery
Inventories unsanctioned AI tool usage with risk scoring per tool/team.
Human-in-the-Loop Efficacy
Measures whether HITL checkpoints actually change outcomes versus rubber-stamping.
Autonomy Graduation Readiness
Decides whether an AI workflow has earned the right to move from supervised to autonomous operation.
Multi-Agent Check Bypass Detection
Detects when multiple AI agents converge on outcomes that bypass intended controls.
Tool Call Chain Risk
Scores risk in agent tool-call chains based on depth, side-effect surface, and rollback feasibility.
AI Output Attribution Chain
Tracks which prompt, model version, retrieval source, and human edits produced a given output.
Synthetic Data Provenance
Detects synthetic content used as training input or evidence with provenance tracking.
Appeal Process Documentation
Captures the customer's appeal channel, response SLA, and review record.
Fine-Tuning Data Leakage Risk
Scores extraction risk of confidential or PII training data from fine-tuned models.
Agent Termination Safety
Pre-deployment scoring of whether an agent can be safely halted mid-execution.
LLM Jailbreak Resilience
Tests deployed AI surfaces against jailbreak corpus and tracks resilience drift.
AI Vendor Lock-In Exposure
Scores switching cost across model providers including prompt portability and contract terms.
AI Incident Postmortem Completeness
Scores AI incident postmortems against structured template.
How It Works
Three steps to structured, auditable decisions.
Discover & Score Risk
Run Shadow AI Discovery to inventory unsanctioned tools, then score each agent with Agent Authority Boundary and Prompt Injection Exposure to quantify your attack surface and authority gaps.
Validate & Harden
Stress-test with LLM Jailbreak Resilience, verify RAG Grounding Quality for hallucination risk, and audit Tool Call Chain Risk to ensure agent workflows can be safely rolled back.
Monitor & Govern
Track AI Evaluator Calibration for scoring drift, measure Human-in-the-Loop Efficacy to prevent rubber-stamping, and detect Multi-Agent Check Bypass before controls are circumvented.
Graduate & Audit
Use Autonomy Graduation Readiness to decide when workflows can move from supervised to autonomous. Maintain full provenance with AI Output Attribution Chain and Model Provenance Attestation.
How many AI agents in your organization have been tested against prompt injection, have verified provenance, or have measured whether human oversight actually changes outcomes?
Spreadsheet AI inventories
Static model lists with no risk scoring, authority boundaries, or jailbreak testing
Manual compliance documentation
No automated provenance attestation or output attribution chain
MLOps tools without governance
Track model versions but not prompt injection exposure, HITL efficacy, or agent authority
GRC add-on modules
Generic risk tools that don't understand agent tool chains, RAG grounding, or multi-agent bypass risks