AI Security
In-depth guides and analysis on ai security from the Safeguard engineering team.
676 articles
Dependency Confusion: Griffin AI vs Mythos
Dependency confusion is older than most of the AI tooling trying to detect it. The attacks have adapted to the defences — detection needs to keep up.
Griffin AI vs Poolside for Enterprise Security
Poolside's on-prem code AI is a credible enterprise offering. For security-specific workflows, Griffin AI's grounding architecture targets different ground.
MCP Authentication Patterns for Enterprise
Enterprise MCP deployments need more than a static API key. The protocol is evolving toward OAuth 2.1 and dynamic client registration, and understanding which pattern fits which workload decides whether your rollout survives the first audit.
Enterprise AI Red Team Program Design
AI red teaming is not a one-off exercise. Programmatic red-teaming of AI systems requires specific structure — and most organisations don't have it yet.
Elastic Scale Behaviour: Griffin AI vs Mythos
Scanning bursts when a monorepo merges. We explain why Griffin AI absorbs the spike gracefully while Mythos-class tools degrade into rate-limit queues.
Griffin AI vs Open Weights: The Eval Gap
Frontier models pass eval benchmarks that open-weight models miss by specific measurable margins. For security workflows, the gap matters.
The Reproducibility Crisis In AI Security Evals
ML research has a reproducibility crisis. AI security evaluation inherits it. Vendors publishing numbers that can't be reproduced are the norm — not the exception.
Griffin AI vs Claude Prompt Caching: Security
Claude's prompt caching gives you 90% discount on cached tokens. Security workloads have massive cacheable surface area. Griffin AI takes advantage; direct API use often does not.
Auth Bypass Discovery: Griffin AI vs Mythos
Auth bypasses are rarely a single bug. They live in the interaction between layers — middleware, route handlers, framework annotations. Finding them requires path analysis across abstraction layers.
Coordinated Disclosure With Upstream Maintainers
Coordinated disclosure with open-source maintainers is a relationship business. Here is what makes it work in 2026, with the artefacts a modern pipeline gives you.
Chain-Of-Thought For Vulnerability Reasoning
Chain-of-thought helps LLMs with multi-step problems. For vulnerability reasoning, it helps — but only when the chain is grounded in structured evidence.
Eval Harness As Release Gate For AI Features
Shipping AI features without an eval harness is shipping without tests. Here is how to build one that actually gates releases without becoming a bottleneck.