Devsecops

Corgea delivers an 80% reduction in remediation effort — not by detecting vulnerabilities faster, but by generating the code fix as a pull request. The traditional SAST workflow is: scan → find vulnerability → file ticket → developer manually writes the fix → PR review → merge. Corgea changes step three onward: scan → AI agent analyzes finding with full codebase context → generates fix code → opens PR for developer review. The AI application security market is projected to reach $5 billion by 2027, and the core problem Corgea addresses is real: codebases are growing faster than security headcount can keep pace. Traditional SAST tools generate false positive rates high enough that developers treat alerts like spam. Corgea’s AI-native approach — not a rule engine with AI bolted on — produces contextually accurate fixes that reduce alert fatigue alongside vulnerability count. ...

SonarQube has 6,500+ static analysis rules and a 24% lower vulnerability rate reported by teams using AI Code Assurance — but AI CodeFix, the feature that generates fix suggestions for detected issues, is only available in Enterprise Edition (starting at $16,000/year for server) or Team plan and above for Cloud ($32/month). That pricing asymmetry defines the honest assessment: AI CodeFix is a value-add layer for organizations already running SonarQube at enterprise scale, not a reason to adopt SonarQube from scratch. Here’s what it actually does, where it falls short compared to AI-native code review tools, and who should use it. ...

Sweep AI is a GitHub App that converts issues into pull requests autonomously — you add a sweep label to an issue, and Sweep analyzes the codebase, writes a plan, generates code changes, and opens a PR. With 7,600+ GitHub stars (Apache-2.0), a 92% issue resolution rate in controlled evaluations, and a free tier that starts at $0 versus Devin’s $500/month, it occupies a specific and defensible niche. Here’s whether it’s the right tool for your team in 2026. ...

AI-generated code contains security vulnerabilities 3.2× more frequently than human-written code, according to Snyk’s 2026 State of AI Code Security report. Static Application Security Testing (SAST) tools that were designed for human-written code are scrambling to keep up with the patterns that LLMs introduce: hallucinated API calls, incomplete error handling, missing authentication checks, and prompt injection surface areas that didn’t exist three years ago. The best tools in 2026 have adapted. Here’s how the top four — Snyk Code, Semgrep, Checkmarx, and Corgea — compare on the dimensions that actually matter for modern development teams. ...

RunSybil is an AI-native offensive security platform that autonomously chains IAM misconfigurations, container escapes, and CI/CD secret exposures into full attack paths — operating black-box against live cloud environments the same way a real attacker would, with no source code or agent credentials required. What Is RunSybil? The AI-Native Pentesting Platform Explained RunSybil is an AI-native penetration testing platform founded in 2023 by Ari Herbert-Voss — OpenAI’s first security research hire — and Vlad Ionescu, formerly of Meta’s Red Team X. The company raised $40M in a Series A in March 2026, backed by Khosla Ventures, the Anthropic Anthology Fund, Menlo Ventures, Conviction, and Elad Gil, with angels from OpenAI, Palo Alto Networks, Stripe, and Google. The product centers on an autonomous AI agent called Sybil that operates against live cloud environments in pure black-box mode — no source code, no privileged credentials, no static playbook. Sybil observes what access it can gain, adapts its attack path accordingly, and chains multiple vulnerability classes together the way an actual human attacker would. This is a fundamentally different model from legacy automated scanners that run pre-defined scripts or check configuration against a compliance checklist. The platform specifically targets the attack surface that dominates modern cloud breaches: IAM misconfiguration, non-human identities (NHIs), container workloads, and CI/CD pipeline secrets — the four categories that together account for over 80% of cloud security incidents in 2026. ...