TL;DR Three application security KPIs cut through alert noise to reveal actual risk: viable attack count, vulnerability escape rate and application coverage completeness. Unlike traditional metrics that measure alert volume, these KPIs leverage graph intelligence to correlate attacks with confirmed vulnerabilities at runtime, achieving verified accuracy while reducing investigation time ... Read More

TL;DR AI code assistants accelerate development velocity, with 46% of code now completed by tools like GitHub Copilot. This speed creates a security challenge: vulnerabilities reach production faster than traditional scanning can catch them. The solution is to adapt security approaches to match development velocity through runtime visibility that monitors ... Read More

TL;DR AI coding assistants can hallucinate package names, creating phantom dependencies that don't exist in official repositories. Attackers exploit this predictable behavior through slopsquatting, which involves registering malicious packages with names that AI models commonly suggest. This emerging supply chain attack requires new detection approaches focused on behavioral analysis to ... Read More

There’s a major problem in application security: Organizations secure code before release, but attackers strike in production. This gap is exactly where runtime application security comes in ... Read More

Key Takeaways Contrast's instrumentation-based approach delivers continuous security validation throughout the SDLC without slowing DevOps workflows Customers can achieve approximately 93% accuracy with false-positive rates as low as 7% vs. 40-60% false-positive rates in traditional automated scanners Real-time detection with code-level guidance accelerates remediation from weeks to hours with actionable ... Read More

Security teams are drowning in vulnerabilities they'll never fix. While organizations scan more frequently and hire more engineers, the fundamental math remains broken: development creates security flaws faster than anyone can remediate them ... Read More

TL;DR AI-powered vulnerability remediation often fails because it lacks context about how your applications actually work. Runtime intelligence solves this by providing AI with real-world application behavior data, architecture insights, and dependency information. This context-aware approach reduces remediation time by up to 87% while eliminating the false positives that plague ... Read More

TL;DR Applications face thousands of attack attempts monthly, yet traditional security tools miss the ones that matter most. New data from Contrast Security reveals that while WAFs, EDR, and SIEM platforms excel at their designed functions, they cannot see inside application runtime where sophisticated attacks actually execute. Runtime visibility fills ... Read More

TL;DR Traditional security tools generate overwhelming false positives because they cannot tell which Log4j vulnerabilities are actually exploitable. This guide explains why IAST runtime detection provides accurate results and how ADR blocks attacks while you patch. You'll learn a proven 5-step remediation strategy and discover why this threat remains relevant ... Read More

DAST vs AI Code: TL;DR Traditional DAST requires 8+ hours for comprehensive scanning while AI-generated code deploys 10 times daily. This speed mismatch creates a fundamental security gap: most organizations run DAST weekly, leaving 70+ deployments unchecked between scans. The Contrast Runtime Security Platform offers a fundamentally different approach by ... Read More