Regulatory alignment

Built for what regulators require.

Every major AI regulatory framework requires evidence that your model behaves consistently with its stated policies. contradish provides automated testing, scored results, and documentation structured for regulatory review.

EU AI Act

Regulation (EU) 2024/1689 · In force August 2024

✓ Addressed

The EU AI Act is the world's first comprehensive AI law. High-risk AI systems must undergo conformity assessment demonstrating accuracy, robustness, and behavioral consistency before deployment in the EU market.

Article 9 · Risk management system

High-risk AI systems must have a risk management system that includes testing to identify failure modes, including failures arising from interactions with the real-world range of users and inputs.

contradish: Tests your model across 16 phrasing variants per policy area, identifying the specific inputs that trigger policy failures.

Article 15 · Accuracy, robustness, and cybersecurity

High-risk AI systems must be resilient to attempts to alter their intended behavior. Providers must demonstrate consistent performance across varied inputs.

contradish: Measures consistency score across direct, indirect, emotional, hypothetical, and adversarial phrasings of the same request. Documents every failure.

Article 13 · Transparency and provision of information

Providers must document the intended purpose, limitations, and performance characteristics of high-risk AI systems, including known failure modes.

contradish: Delivers a full PDF and JSON report documenting every tested policy, every failure found, and the remediation for each. Designed to be shared with regulators and technical documentation.

NIST AI Risk Management Framework

AI RMF 1.0 · Published January 2023

✓ Addressed

The NIST AI RMF is the US federal standard for responsible AI development. It is referenced in Executive Order 14110 and adopted by federal agencies via OMB M-24-10. Its MEASURE function requires systematic testing and evaluation of AI systems.

MEASURE 2.5 · AI system testing

The AI system to be deployed is demonstrated to be valid and reliable through tools and techniques such as evaluations, red teaming, and adversarial testing.

contradish: Runs automated red teaming across selected policy domains. Produces a consistency score and full failure inventory meeting the documentation standard for MEASURE 2.5.

MEASURE 2.6 · Evaluation of AI systems in operation

The risk or impact of the AI system on individuals or groups is evaluated at regular intervals and after incidents to verify that the system continues to perform as intended.

contradish: Designed for recurring evaluation. Results are timestamped and versioned so teams can demonstrate ongoing compliance and track improvement over time.

MAP 5.1 · Likelihood and impact of harm

Likelihood and magnitude of each identified impact based on anticipated use, organizational context, and historical information are evaluated and documented.

contradish: Weights failures by domain severity. Failures in mental health, healthcare, and safety-critical domains are weighted higher, producing a risk-calibrated score rather than a flat pass rate.

Executive Order 14110

Safe, Secure, and Trustworthy AI · October 2023

✓ Addressed

EO 14110 directs federal agencies to adopt rigorous AI safety standards and requires AI developers to share safety testing results with the US government. It specifically mandates red teaming for foundation models and safety-critical AI systems.

Section 4.1(a) · AI safety standards

Requires developers of AI systems that pose risks to national security, economic security, or public health to share the results of safety testing before public deployment.

contradish: Produces a structured JSON and PDF report of safety evaluation results formatted for sharing with government oversight bodies.

Section 4.2 · Red teaming requirements

The order directs NIST to develop guidelines and standards for AI red teaming and requires red teaming of dual-use foundation models prior to public release.

contradish: Provides automated policy red teaming aligned with NIST red teaming guidance, systematically probing model behavior across adversarial phrasings.

OMB Memorandum M-24-10

Advancing Governance, Innovation, and Risk Management for Agency Use of AI · March 2024

✓ Addressed

M-24-10 establishes minimum practices for federal agencies deploying AI that affects rights or safety. It requires agencies to test AI for bias, document limitations, and verify that deployed AI behaves as intended before and during operation.

Section 5 · Minimum practices for rights- and safety-impacting AI

Agencies must test AI systems for performance on diverse populations and document known limitations and failure modes before deployment. Post-deployment monitoring is required.

contradish: Tests across 16 phrasing variants including emotional, indirect, and casual registers that reflect real user diversity. Produces a failure inventory that constitutes documented evidence of known failure modes.

FDA AI/ML Guidance

AI/ML-Based Software as a Medical Device (SaMD) · Ongoing regulatory framework

✓ Addressed

The FDA requires AI-enabled medical devices to demonstrate consistent, reliable performance and to have predetermined change control plans with ongoing performance monitoring. Any AI product that influences clinical decision-making falls under this framework.

Performance validation · Pre-market requirement

AI/ML-based SaMD must demonstrate that the system behaves as intended across the range of inputs it will encounter. Validation testing must be documented and reproducible.

contradish: Provides reproducible, timestamped evaluation reports documenting model behavior across a defined test suite. Results are exportable for inclusion in 510(k) or De Novo submissions.

Ongoing performance monitoring · Post-market requirement

After deployment, developers must continuously monitor AI performance and detect drift from intended behavior. Real-world performance must be compared against pre-deployment testing.

contradish: Designed for recurring evaluation. Teams run the same suite after model updates to detect regression and maintain documented evidence of ongoing compliance.

ISO/IEC 42001

AI Management System Standard · Published December 2023

✓ Addressed

ISO 42001 is the international standard for AI management systems, analogous to ISO 27001 for information security. It provides a certifiable framework for responsible AI development and is increasingly required by enterprise procurement and government contracting.

Clause 9.1 · Monitoring, measurement, analysis, and evaluation

Organizations must evaluate AI system performance using methods that yield valid results. Monitoring must be documented and reviewed at planned intervals.

contradish: Produces structured, repeatable evaluation results with full methodology documentation, meeting the evidentiary standard for Clause 9.1 monitoring records.

Clause 6.1 · Actions to address risks

Organizations must identify, assess, and plan responses to AI-related risks. Risks from AI system behavior inconsistent with intended use must be documented and addressed.

contradish: The failure inventory produced by each evaluation feeds directly into risk registers, documenting specific behavioral risks and their remediation status.

FTC AI guidelines

Federal Trade Commission · AI and Algorithmic Accountability

✓ Addressed

The FTC enforces consumer protection law against AI products that make false or misleading claims. If your AI product states it follows certain policies or safety rules, the FTC expects you to have tested and verified those claims.

Section 5 FTC Act · Unfair or deceptive acts or practices

AI companies that claim their products are safe, compliant, or policy-adherent must have substantiation for those claims. Marketing claims about AI safety without testing constitute deceptive trade practices.

contradish: Provides the documented test evidence that substantiates policy compliance claims. If your product states it follows safety or content policies, contradish testing constitutes substantiation.