Philip Walley | The Secure Edge

Sign in Subscribe

AI

The Next Access Review Won’t Be for People. It’ll Be for AI Agents

The Next Access Review Won’t Be for People. It’ll Be for AI Agents

Every quarter, enterprises ask familiar questions about human access. Does this user still need access to this application? Does this admin still need elevated privileges? Is this service account still active? Are these permissions still aligned to the person’s role? What access should be revoked before it becomes a

Extending Zero Trust into AI: An Architect's View

Extending Zero Trust into AI: An Architect's View

Something shifted at RSAC 2026 last week. The conversations that previously orbited around "how do we secure AI" moved toward something more specific and more urgent: how do we extend Zero Trust, the framework most enterprises have been building toward for the better part of a decade, to

The Agentic AI Security Crisis Is Here, and OpenClaw Proved It.

The Agentic AI Security Crisis Is Here, and OpenClaw Proved It.

OpenClaw became the most-starred repository in GitHub history in about 60 days. By early March, it had passed 250,000 stars, overtaking React, a project that took over a decade to reach that number. If you work in enterprise security or architecture and haven't heard of it yet,

AI Just Did What Hiring Never Could for Cybersecurity

AI Just Did What Hiring Never Could for Cybersecurity

We've been talking about the cybersecurity workforce shortage for over a decade now. At this point, it's almost background noise in the industry. Someone publishes the ISC2 numbers, we all read the headlines, nod along, and go back to work. Millions of unfilled roles globally, the

Private AI Security: Protecting Enterprise AI Systems and Sensitive Data

Private AI Security: Protecting Enterprise AI Systems and Sensitive Data

Key Takeaways * Combining data privacy protection with AI system safeguards enterprise AI implementations from unauthorized access and data breaches * Organizations implementing private AI must address unique challenges, including model isolation, data residency requirements, and secure inference pipelines * The NIST AI Risk Management Framework, ISO/IEC 27001, and emerging standards like

Zero Trust AI Security: Securing the AI You Use and the AI You Build

Key Takeaways * Zero trust AI security requires distinct approaches for AI systems that enterprises consume versus AI systems they develop internally * Traditional perimeter-based security measures fail to protect distributed AI workloads across multi-cloud environments and third-party services * Identity-centric controls for AI models, data pipelines, and inference endpoints are becoming as

AI Security Zero Trust: Comprehensive Protection for Modern AI Architectures

Key Takeaways * Zero trust security model provides essential protection against unique AI threats, including adversarial attacks, data poisoning, and model theft that traditional security approaches cannot adequately address. * AI architectures with multiple models require granular access controls, continuous monitoring, and microsegmentation to secure data flows between components like training pipelines,