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.
AI agents pull tools, models, and data from a sprawling chain of upstream providers. In 2026 attackers learned to poison that chain — and the fallout is shaping how enterprises buy and operate agentic systems.
The Model Context Protocol enables AI agents to interact with external tools and data sources. Securing MCP servers requires authentication, authorization, and input validation patterns specific to the AI agent context.
Automated fix pull requests sound great until half of them fail CI. Here is how to ship auto-PR remediation that keeps the green build, every time.
Copilot, Cursor, and Claude Code change what enters your codebase and how. A practitioner's map of the real supply chain risks — hallucinated packages, rules-file injection, and unreviewed transitive trust.
DSPM for AI closes the gap traditional tools miss: tracking sensitive data through embeddings, fine-tuning, and vector stores to meet EU AI Act and Colorado AI Act requirements.
Anthropic's Mythos model is generating buzz for AI-powered vulnerability detection. We break down what it does well, where it struggles, and why security teams should approach the results with healthy skepticism.
Anthropic's Mythos model claims to find vulnerabilities in open-source code using a single LLM. We analyze where this approach falls short and why production-grade zero-day discovery requires Safeguard's Multi-Agent TAOR Deep Think AI Engine.
Pervasive AI means models embedded in nearly every application and workflow. That ubiquity creates a security surface most programs have not mapped. Here is how to think about it.
Large language models are being used to find vulnerabilities in open-source code. But a single model, no matter how capable, isn't enough. Here's why multi-agent orchestration, structured CWE analysis, and deep context matter more than model size.
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