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.

The cybersecurity landscape is facing an unprecedented shift, and industry experts are sounding the alarm about what many are calling the “vulnpocalypse.” This isn’t just another security buzzword or overhyped threat. It represents a fundamental transformation in how vulnerabilities are discovered, exploited, and defended against in the age of artificial intelligence.

Engineering teams today face a dual mandate: ship high-quality features faster while keeping the underlying infrastructure secure. As development velocity increases, so does the complexity of the tools, libraries, and third-party components that make up your applications. The challenge? Your application’s security is now tied to a vast supply chain of code you didn’t write.

The productivity revolution promised by AI coding assistants has arrived. Developers are shipping features faster than ever, with tools like GitHub Copilot, Amazon CodeWhisperer, and Claude Code becoming as essential to modern development as Git itself. But beneath this velocity lies a troubling reality that every security leader needs to confront: we’re scaling security debt at unprecedented speed.

In the race to ship software faster, security teams are drowning. Not in vulnerabilities… those are abundant, predictable even. They’re drowning in noise. The average enterprise application generates thousands of security findings from multiple scanners, each screaming for attention with equal urgency. Meanwhile, developers are building faster than ever, fueled by cloud-native architectures, open-source dependencies, and AI-generated code. The uncomfortable truth?

Modern development teams move fast; security must keep pace. As organizations increasingly rely on GitLab to power CI/CD pipelines, integrating application security directly into the workflow is no longer optional — it’s essential. The Veracode GitLab Workflow Integration embeds automated security testing directly into GitLab pipelines, enabling teams to shift security left without disrupting delivery.

The revolution is here, but it’s not what we expected. AI coding assistants have transformed software development, with developers shipping code faster than ever before. GitHub Copilot, Amazon CodeWhisperer, and Claude Code have become as essential to modern development as Git itself. The productivity gains are undeniable; what once took hours now takes minutes. But there’s a dangerous blind spot in this revolution: security.