All articles
-
Self-Driving Car Accident Causes: What Crash Data Shows
Sensor failures, software edge cases and fault attribution behind self-driving car accident causes, plus what NHTSA Standing General Order data shows.
-
AI Incident Database Comparison: Which One to Use
AIID, OECD AIM, AIAAIC, AVID and MITRE ATLAS all answer to the name AI incident database. What each one records, and which to reach for when.
-
AI Incident Reporting Requirements: Who to Notify
One AI incident can start four legal clocks at once. The EU AI Act, GDPR, SEC and sector regulators each want a different notice on a different deadline.
-
Real World AI Failure Examples That Triggered Lawsuits
Real world AI failure examples, from a wrongful arrest in Detroit to a chatbot advising glue on pizza, and the court rulings and lessons they produced.
-
Best AI Monitoring Tools 2026: LLM Observability Compared
A practitioner's guide to the best AI monitoring tools for 2026, covering LLM observability platforms, ML drift detection, pricing, and how to choose.
-
Prompt Injection Attack Explained: How Attackers Hijack LLMs
A technical explainer of how prompt injection attacks work, the direct and indirect attack classes, real consequences, and what defenders can do.
-
Deepfake Detection Tools Review: Platforms Compared
A deepfake detection tools review of Reality Defender, Intel FakeCatcher, Pindrop Pulse, Sensity AI, and Amber Authenticate, with a buyer decision matrix.
-
Reconstructing an Incident Timeline From Primary Sources
A vendor advisory, a CVE record, a regulator filing and a researcher's blog post all date one event differently. How to reconcile them into a timeline.
-
An Incident-Response Playbook for AI Systems
Generic IR runbooks assume the failing component is a server you can patch. AI incidents add a model whose behavior you can't fully explain.
-
Anatomy of a Vendor Advisory: Reading What Isn't Said
Vendor advisories from AI providers follow a recognizable shape. Knowing what to look for, and what is left out, turns marketing into usable signal.
-
AI Taxonomy: Incident, Vulnerability, Disclosure, Misuse
A working taxonomy that distinguishes AI incidents, vulnerabilities, disclosures, and misuse by impact, weakness, publication, and intent.
-
NVD CVE Entries for ML Libraries: What Fields Mean
NVD entries for torch, transformers, vLLM, and LangChain are not written for ML engineers. How to read the fields and what to do when metadata is thin.
-
Reading a Model Card for Security Signals
Model cards are written for researchers, not defenders. What to read first, in what order, to judge a model's security posture and disclosure practice.
-
Source Verification Tiers: Vetting an AI Incident Claim
A five-tier source ladder for verifying AI security incident claims, with evidence thresholds and examples of claims accepted or rejected.
-
Why We Don't Do Attribution Speculation on AI Incidents
Attribution is the slowest, hardest, most consequential call in incident reporting. Here's the policy that keeps us from getting it wrong.
-
How We Log AI Security Incidents: Our Methodology
The methodology behind AI Incidents — how we verify sources, date-stamp claims, and decide what's news vs noise in the AI security incident beat.