Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As many of you may know, MITRE’s DHS contract to manage the CVE and CWE programs expired on April 16, 2025. While emergency funding has since been restored for a short time, the long-term future of these programs still remains uncertain. Understandably, this situation has raised concerns throughout the cybersecurity community about the stability and continuity of vulnerability tracking and management systems that many organizations have come to rely upon.

Security teams today face increasing pressure to quantify the effectiveness of their application security programs. Whether it’s justifying security investments to leadership or demonstrating compliance with regulations like PCI DSS, HIPAA, and GDPR, teams struggle to showcase the real impact of their security efforts. Without clear, actionable data, proving that an AppSec program is actively reducing risk becomes a challenge. That changes today.

Every hype cycle brings fresh security concerns, and AI is no exception. AI governance might sound like uncharted territory, but it’s really just another evolution of the same security principles AppSec teams have been applying for years. The fundamentals—secure coding, risk management, compliance, and policy enforcement—haven’t changed.

Lately, it seems like the only thing anyone is talking about in the technology sector is Artificial Intelligence. With good reason! AI is an incredibly powerful tool that is only going to grow in usage and scope. However, there seems to be a lot of confusion around various terms involving AI and security. The focus of this blog will be breaking down the differences between securing AI, secure AI use, AI for security, and AI safety.