ai-security
Safeguard articles tagged "ai-security" — guides, analysis, and best practices for software supply chain and application security.
593 articles
AI Coding Assistant Data Leakage Paths
AI coding assistants promise productivity but expand the data leakage surface in specific, mappable ways. The paths, the mitigations, and what enterprise policy actually looks like.
Real-World Vs Synthetic Eval Gap In Security
Synthetic eval benchmarks are controllable. Real-world data is messy. The gap between performance on each is usually large, and vendors prefer one over the other for a reason.
Bulk Remediation Of Aged Vulnerability Backlog
Most security teams are sitting on hundreds of stale findings. Here is how to clear an aged vulnerability backlog with bulk remediation that actually merges.
Cryptography Misuse Detection: Griffin AI vs Mythos
Crypto misuse is not about broken algorithms. It is about misused parameters, missing checks, and the gap between "it compiles" and "it is secure."
What Is Explainable AI? A Security Practitioner's Guide
Explainable AI makes model decisions inspectable so security teams can trust, audit, and defend them. Here is what that means in practice.
Ensemble LLMs For High-Precision Security Findings
One model's confident answer is a guess. Multiple models agreeing is evidence. Ensemble approaches raise precision for security-critical findings.
Typosquatting Meets AI: The New Threat of AI-Generated Package Names
AI code assistants recommend packages that do not exist, and attackers are registering those hallucinated names. This new typosquatting vector exploits the trust developers place in AI suggestions.
Security Testing for LLM-Powered Applications
Applications built on large language models introduce novel attack surfaces that traditional security testing does not cover. This guide addresses the specific testing methodologies needed for LLM applications.
Hallucinated Security Findings: Measurable Rates
Pure-LLM security analysis hallucinates findings at rates between 20% and 70% depending on the task and model. Grounding is the architectural answer.
False Positive Rates: Griffin AI vs Mythos Benchmarked
Why pure-LLM security products generate false positives that engine-grounded platforms like Griffin AI structurally cannot — with CWEs and real triage data.
Cursor Enterprise Security Buyer Review 2026
An honest security buyer's review of Cursor Enterprise for 2026: data handling, model isolation, audit posture, and the gaps to negotiate before signing.
AI Data Security Solutions: What Actually Protects Your Data?
AI data security solutions cover the tools and controls that protect the data flowing into, through, and out of AI systems. Here is what the category really includes and how to evaluate it.
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