Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Developers love AI coding tools. And why wouldn’t they? After all, they write code faster. They reduce repetitive work. They help junior engineers ship features that used to take days. But there’s a problem no one wants to talk about at the planning meeting. AI coding tools are producing insecure code at massive scale. And the industry is running out of time to fix it.

Every few years, something enters the market that doesn’t just change the conversation — it restructures the underlying assumptions of an entire industry. The rapid advancement of AI systems purpose-built for software and security workflows is one of those moments. And I think most of the market is still misreading what it actually means. There will be no shortage of takes. Some will declare that AI has finally “solved” software security.

We are living in a security paradox. Cybersecurity budgets are increasing, security stacks are growing more complex, and yet, the needle barely seems to move. According to the newly drafted 2026 Cyberthreat Defense Report (CDR), 81% of organizations experienced at least one successful cyberattack this past year. Even more concerning, the number of organizations suffering from six or more successful attacks is actually creeping up.

For years, the annual penetration test or quarterly security scan served as the cornerstone of application risk assessments and application risk management. Teams would run the assessment, triage the findings, hand the report to developers, and wait for the next cycle. It felt like progress. It wasn’t.