Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

There’s a conversation happening in boardrooms, security operations centers, and developer standups that I find both thrilling and concerning: the conversation about AI-assisted development. Engineering teams are shipping features in hours that once took months. Products that would have required six-month roadmaps are being prototyped in a weekend.

Modern software is not written from scratch. It’s assembled. Developers pull from open-source repositories, import third-party libraries, accelerate development with AI coding assistants, and deploy across multi-stage CI/CD pipelines that span dozens of tools, services, and vendors.

Twenty years ago, the idea of continuously scanning software for vulnerabilities at scale was ambitious. Today, it’s essential. As Veracode marks its 20th anniversary, we’re not just looking back at what we’ve built; we’re looking forward at what the data tells us about where software security needs to go next. And the data says a lot.

The rules of software security have changed. For years, the dominant threat narrative centered on stolen credentials and compromised accounts. Today, attackers have shifted strategies — and the data proves it. According to the 2026 Verizon Data Breach Investigations Report, exploitation of vulnerabilities now accounts for 31% of all initial access vectors, surpassing credential abuse, which has fallen to just 13%. Attackers aren’t just knocking on the front door anymore.

Every year, the Verizon Data Breach Investigations Report sets the tone for how the industry understands the threat landscape. And every year, the most important question isn’t what’s changed — it’s whether organizations are keeping up. Based on the 2026 Verizon DBIR, the honest answer is: not fast enough.

The security landscape has fundamentally changed, and many organizations haven’t caught up. If you’re still relying on quarterly scans, annual penetration tests, or spreadsheet-based vulnerability tracking to manage risks within your applications, you’re not managing risk. You’re documenting it after the fact.

May 19, 2026 What the 2026 Verizon DBIR Reveals About the State of Application Security Read More Natalie Tischler May 14, 2026 How to Manage Risks Within Your Applications Read More Natalie Tischler May 12, 2026 AI Coding Tools Are Creating a Security Gap We Must Close Immediately Read More Natalie Tischler.

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.