Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The enterprise security world is having two separate conversations that desperately need to collide. On one side, application security (AppSec) teams are scrambling to secure APIs – the connective tissue of every modern application. On the other, a new wave of “AI security” vendors promise to protect your LLMs from prompt injection, data leakage, and hallucinations. Both groups are solving real problems. Both are missing half the picture.

Let’s start with a scenario. This is illustrative, not a single reported incident. A developer shares a Postman collection in Slack to move faster. “Here’s the Postman collection for the payment API. It has live auth headers so you can test prod endpoints.” The team uses it, work gets done, and the link stays. What no one realizes is that the collection lives inside a public Postman workspace. Weeks later, it is indexed by search engines. The URL requires no login.

Many organizations have hundreds or thousands of API endpoints across their services, each of which handles authentication differently. For example, one service might rely on standard headers like Authorization: Bearer, while another uses an API key, and a third uses a custom JSON Web Token header with mechanisms or naming conventions specific to the team that built it.

A newly disclosed security issue, tracked as CVE-2026-44578, affecting Next.js applications is raising concerns across the developer and security communities after researchers identified multiple authorization bypass and middleware evasion paths that could expose protected application data and credentials. The vulnerabilities impact several versions of Next.js and allow attackers to bypass middleware-based authorization controls using crafted requests and route manipulation techniques.

Editor's note: This article was originally published by Craig Riddell on LinkedIn. It has been republished here with the author's permission. Boards are giving AI security more airtime than ever. What they're not giving is the right framing. A year or two ago, AI was mostly a question of experimentation risk. Today, it's tied directly to revenue, customer experience, operational efficiency, and competitive advantage. The urgency is real, and it's translating into aggressive deployment timelines.