Securing trust at the edge

Trust isn't a login screen anymore. With Al shaping dynamic risk scoring, edge-native enforcement, and session-aware Zero Trust, enterprises are rewriting how collaboration and remote work stay both fast and secure

- PCQ Bureau

In cybersecurity, trust used to be a static checkpoint. Log in, prove your device is healthy, and you're good to go. But in a world of 3 a.m. logins from unexpected locations, shadow IT apps spinning up in seconds, and employees collaborating across borders, that model breaks down fast.

As Prakash Mana, CEO, Cloudbrink, explains, AI is redefining how enterprises think about trust. It's not about locking the gates tighter. It's about evaluating context and behavior in real time, making decisions at the edge, and letting legitimate work flow without slowing people down.

Trust scoring that never stands still

Traditional Zero Trust focuses on identity and device posture. AI takes it further by continuously learning from behavioral and contextual signals: login times, access locations, application usage, and data movement.

If a developer suddenly downloads gigabytes of files at an odd hour from a new geography, the system doesn't wait for a scheduled review. It adapts instantly, adjusting trust and stepping in. This approach doesn't just block threats; it ensures legitimate activity continues without friction.

Deep-tech powering continuous enforcement

The shift to distributed work and microservices means policies can't stop at login. Enforcement has to be constant, precise, and close to the application itself.

Three innovations stand out:

eBPF: Hooks directly into the Linux kernel to monitor and enforce policies in containerized environments.

Service mesh: Platforms like Istio stitch Zero Trust into microservice communication using mutual TLS and fine-grained controls.

AI-powered policy engines: Replace static rules with adaptive enforcement based on risk, workload sensitivity, and behavior.