DevSecOps

Answer-first: Securing AI-generated software requires hardening development pipelines against unique attack vectors: indirect prompt injection via pull request comments, poison tokens in training corpora, slopsquatting dependency insertion, and copyleft license contamination. By enforcing zero-trust container sandboxing, cryptographic dependency provenance verification, and real-time AST token sanitization, enterprise security teams insulate production environments from adversarial exploitation during autonomous code synthesis. Prerequisite: Deep understanding of application security fundamentals, OWASP threat modeling, cryptographic signing (Sigstore/Cosign), Git commit signing, and continuous integration execution isolation is assumed. ...

Answer-first: As AI agents gain autonomous tool execution privileges (reading databases, modifying infrastructure, pushing code), the security perimeter shifts from network boundaries to Instruction Integrity. Modern AI Security Engineering establishes Seven Layers of Defense, enforcing the Dual-LLM Pattern for indirect prompt injection immunity, Policy-as-Code (OPA/Rego) for runtime authorization, and Zero Data Retention (ZDR) compliance. 1. The OWASP Top 10 for LLMs & Agentic Systems (2026 Update) Autonomous agents introduce unprecedented attack surfaces where natural language text acts as executable code. The OWASP Top 10 for LLM Applications (2026) highlights the most critical enterprise threats: ...