Expert perspectives on application security, compliance, and emerging threats
GeneralYou can t audit what you can t define. When your LLM returns a user profile, incident report, or configuration change, you need to know exactly what fields you re getting, what types they are, and whe
GeneralAI spending is projected to reach $2.5 trillion by 2026, with 40% of enterprise applications embedding task-specific AI agents by the end of that year. However, many organizations grant these agents m
GeneralYou ve probably seen the demos: type a single command, watch an AI agent probe your infrastructure, generate exploits, and produce a vulnerability report. Lyrie, the open-source autonomous pentesting
GeneralThe Conventional Wisdom Security teams often treat AI coding agents like advanced code generators. The typical approach: let the agent generate code, then scan the output. This includes static analysi
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GeneralWhen you review a Pull Request, you re looking at changed files. Your SAST tool scans those files. Your IDE highlights issues in the file you have open. But the SQL injection vulnerability? It starts
GeneralThe Conventional Wisdom Your security team runs dependency scanners in CI/CD. You ve configured Dependabot, Snyk, or another tool. When a pull request comes in, the scanner checks for known vulnerabil
GeneralScope This guide focuses on modifying CI/CD pipelines for deploying large language models (LLMs) to production. If your team is running LLM-powered features like chatbots or content generators, your c
GeneralAmazon EKS operates hundreds of thousands of Kubernetes clusters across more than thirty AWS regions. Their recent architectural changes highlight which assumptions about Kubernetes management fail at
GeneralIf you ve ever watched a junior pentester run Nmap without understanding what they re looking for, you ve seen the problem AI-driven security tools must solve. Automation without reasoning creates noi
GeneralYou re deploying AI agents at scale. Your compliance framework covers user access, service accounts, and third-party integrations. But what about the agents themselves? Enterprises expect to deploy an
GeneralThe Conventional Wisdom The industry consensus suggests running AI-generated code in isolated sandboxes before it touches production. Tools like Greptile s TREX feature and Cursor s cloud agents creat
GeneralYou re maintaining a payment processing service that depends on 247 open source packages. Eighteen of them haven t seen a commit in over two years. Three have known CVEs with no patches coming. Your c
GeneralOver the past year, fewer companies are relying on AI systems for penetration testing. This isn t a failure of innovation—it s a market correction after inflated expectations met operational reality.
GeneralYour AI coding assistant just wrote 6,000 lines of code. It compiled. Tests passed. Your CI pipeline is green. But when a verification agent checked those same 6,000 lines against your team s actual r
GeneralScope - What This Guide Covers This guide provides the security controls and response protocols your team needs when deploying large language models (LLMs) in production environments. The rapid jailbr
GeneralThe assumption that you can secure AI in your applications just like open-source libraries—by scanning for vulnerabilities, checking licenses, and updating as needed—is dangerously incomplete. Why Tra
