GPAI models with systemic risk: Articles 51 and 55 explained
How a general-purpose AI model is classed as systemic risk, the compute presumption, and the extra duties: testing, incident reports and security.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
How a general-purpose AI model is classed as systemic risk, the compute presumption, and the extra duties: testing, incident reports and security.
In May 2024 Google explained odd AI Overviews answers: data voids, satire and forum posts. What it said, which examples were faked, and what changed.
An agent followed injected instructions and acted. In the first hour: stop it, revoke its access, preserve logs, scope the damage, then fix the path.
Research shows custom fine-tuning can erode safety alignment, even with benign data. What it means and how to test a tuned model before shipping.
A fake document hidden inside a normal RAG entry can be treated as a separate result. What ATLAS says about it and how to reduce the exposure.
OWASP names three root causes of excessive agency: too much functionality, permission and autonomy. Use this checklist to review each agent you build.
Article 15 asks high-risk AI to resist data poisoning, model poisoning, adversarial examples and confidentiality attacks. What it means for security teams.
Myth: grounding a model with RAG or fine-tuning prevents prompt injection. Fact: OWASP says research shows neither fully mitigates it. What to do instead.
EDR watches processes and files, so it usually cannot read a prompt or judge an agent's intent. Myths and facts about what existing tools miss in AI.
Weekly insights on software supply chain security, delivered to your inbox.
Your first fix PR is minutes away.
No sales call required, even your agent can complete the purchase over MCP.