Your audit function wasn't built for AI. Your evidence shouldn't be either.
AssuranceOps helps organizations produce structured, defensible evidence for AI systems — use case inventories, testing records, oversight documentation, and monitoring evidence that regulators and auditors actually need.
Traditional audit evidence doesn't cover AI risk
Most audit teams are equipped to review access controls, change management, and uptime. They are not equipped to review outcome monitoring, drift detection, bias testing, explainability, or human oversight evidence.
Traditional Audit Checks
AI Assurance Requirements
If your evidence package can't answer these questions, it can't defend your AI systems to a regulator, a court, or a board.
Structured AI assurance evidence — not screenshots, not spreadsheets
AI-003: Claims Triage Model
AI-007: HR Screening Tool
AI-012: Customer Service Chatbot
Four capabilities. One evidence layer.
AI Use Case Inventory
Catalog every AI system in the organization with risk classification, ownership, deployment status, and regulatory applicability. The foundation for knowing what you need to govern.
Testing & Validation Records
Structured evidence of pre-deployment and ongoing testing — bias evaluations, accuracy benchmarks, fairness testing across subgroups, adversarial testing, and red-team exercises. Timestamped, hashed, and mapped to control requirements.
Oversight & Human Review Packs
Evidence that human oversight exists and functions — named reviewers, review frequency, override rates, review duration, escalation triggers, and board-level summaries. Designed to answer: "Can you prove a human meaningfully reviewed this?"
Monitoring & Drift Evidence
Post-deployment evidence — performance baselines, drift detection thresholds, alert configurations, incident logs, and remediation records. Continuous, not point-in-time.
The deadlines are real
EU AI Act
High-risk obligations effective August 2, 2026 — including risk management, technical documentation, human oversight, post-market monitoring, and incident reporting.
NYC Local Law 144
Annual bias audits required for automated employment decision tools. Enforcement tightening after December 2025 Comptroller audit.
NIST AI RMF
Govern, Map, Measure, Manage framework increasingly referenced by US regulators as the baseline for AI risk management.
Colorado AI Act
Takes effect 2026. Requires "reasonable care" to protect consumers from discrimination by high-risk AI systems.
These aren't future requirements. They're current obligations. The evidence needs to exist now.
Start with a 20-minute AI Assurance Diagnostic
We'll map your current audit coverage against AI assurance requirements and identify the gaps. No pitch. Just a clear picture of where you stand.
An AI Guru® Product
Built by the team behind AI Guru — 60,000+ professionals trained, enterprise AI deployed across financial services, manufacturing, media, and healthcare.
Visit AI Guru →Build defensible AI assurance evidence
Start with a diagnostic. Get a clear picture of your coverage gaps.