Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Static application security testing (SAST) tools are intentionally conservative. Traditional scanners identify code that appears exploitable and flag the snippet for review, even when protections elsewhere in the application prevent exploitation. Although that approach helps teams catch vulnerabilities, it also creates false positives that consume developer time, slow remediation efforts, and make future alerts easier to dismiss.

IBM’s 2025 Cost of a Data Breach Report found that 97% of organizations that experienced an artificial intelligence (AI)-related security incident lacked proper access controls on AI systems. The same report highlighted that 63% of organizations lacked governance policies to manage AI or prevent shadow AI. Despite those statistics, AI is now deeply embedded in workflows across critical business functions. Employees are using public AI tools to work faster.

AI-enabled attacks move faster than human analysts can track, at a scale that traditional SOCs weren’t designed to withstand. eCrime breakout times collapsed to 29 minutes on average in 2025, with the fastest clocked at 27 seconds. The rise of frontier AI models is expected to compress the time between vulnerability discovery and exploitation, intensifying pressure on SOC teams. Defending against AI-accelerated adversaries requires a new operating model.