AI Red Team Engineering notes, research references, and field notes on AI systems, responsible AI, governance, and compliance.
Global search

Search AI Red Team

Search across the complete knowledge base.

Enter at least two characters to begin.
↑↓ select Enter open Browse the full research archive
OECD.AI Wonk · September 3, 2026

Can the finance sector oversee AI innovation while maintaining its rapid progress?

analysis AI ComplianceModel EvaluationAgent Security
Why it matters

OECD and FCA authors explain AI Live Testing as discovery workshops followed by review of firms’ testing and monitoring in live financial use cases. Evidence covers architecture, data pipelines, robustness, logging and technical resilience. Firms retain responsibility for tests and risk controls; participation provides feedback, without regulatory approval or audit sign-off. Agentic systems make ongoing monitoring essential because pre-deployment tests cannot cover every path.

My takeaway: Build an evidence pack around a defined use case: system boundaries, data lineage, test results, version history, monitoring and accountable owners. Record how feedback changes controls and how consumer outcomes are checked after deployment. Treat human oversight as a control to evaluate, and avoid presenting programme participation as regulatory certification.
Keep exploring

More curated notes connected through AI Compliance and Model Evaluation.

OpenAI News · framework

Pacing model development in an era of cyber-critical capabilities

OpenAI says preliminary evidence that Astra may meet its Critical cybersecurity threshold led it to pause frontier reinforcement-learning work for two weeks and keep its largest planned run on hold. New safeguards include stronger workload and network isolation, continuous boundary testing, token-level monitoring that escalates suspicious tool activity, and broader alignment checks for deception, reward hacking, and unauthorized access.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

OpenAI News · framework

A blueprint for democratic governance of frontier AI

OpenAI proposes a three-part U.S. frontier-AI governance model: harmonize emerging state safety laws into a federal baseline, strengthen CAISI as an evaluation and standards institution, and coordinate a broader resilience program. Proposed controls include severe-risk evaluations, transparency reports, independent audits, safety-incident reporting, model-weight security, whistleblower protection, and periodic technical assessments.