NIST AI Risk Management Framework
A practical foundation for governing and managing AI risk across the system lifecycle.
Knowledge base / Field references
A curated collection of governance frameworks, threat models and open-source tools for secure enterprise AI.
Standards and operational guidance for accountable AI.
A practical foundation for governing and managing AI risk across the system lifecycle.
European guidance for securing AI systems, operations and processes through layered controls.
Operational resources for testing, evaluation, verification and validation of trustworthy AI.
Models for understanding how AI systems fail and how adversaries exploit them.
The current community-driven reference for critical risks in generative AI applications.
A focused risk model for autonomous systems that plan, use tools and act across workflows.
A knowledge base of adversary tactics and techniques targeting machine-learning systems.
Projects that help teams test, constrain and harden AI systems.
An open-source toolkit for programmable conversational and topical guardrails.
Tools for evaluating and defending against evasion, poisoning, extraction and inference attacks.
An automation framework for identifying risks in generative AI systems through adversarial testing.
GITHUB / RLEALZ
Repositories spanning AI agents, cybersecurity, Web3 and applied product engineering.
LEAL STRATEGY / ADVISORY
Turn public guidance into controls, architecture and an operating model aligned with your organisation's risk profile.