Artificial Intelligence
Who Authorized the AI?
AI authority rarely arrives through one reckless decision. It accumulates through reasonable approvals until no one can say who authorized the workflow.
Zero Trust
Zero Trust
The Breach Is Inevitable. The Blast Radius Is Not.
Why microsegmentation still matters in the AI era, and why the path forward has to be smaller, smarter, and more honest. If you have ever been part of a microsegmentation project, you know the hard part usually does not start with the technology. It starts in a room. Network teams
AI
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
AI
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
AI
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
SASE
Post-Quantum Cryptography: What Enterprise Security Teams Need to Know
I keep hearing about post-quantum cryptography. It comes up in industry reports, vendor announcements, and security news with increasing frequency. My sense is that most security professionals are in the same place I am: aware that something is happening, not entirely sure what it means for them, and uncertain
AI
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
AI
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
AI
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,
SASE
SASE Architecture Zero Trust Framework: Building Modern Cloud Security
As enterprises accelerate cloud adoption with 83% of workloads expected to run in the cloud by 2025, traditional perimeter-based security models are proving inadequate for protecting distributed, hybrid environments. The convergence of network and security services through a secure access service edge, combined with zero trust principles, offers a
AI
AI and Cybersecurity: Top Benefits, Risks, and Defense Strategies
AI is transforming the field of cybersecurity by improving how threats are detected and managed. This article delves into the benefits of AI and cybersecurity, the potential risks involved, and the strategies to effectively integrate AI models into defense mechanisms through behavioral analytics. Organizations can better protect themselves against evolving
AI
Securing the Future: Best Practices for Generative AI Enterprise Security
Generative AI can transform enterprises, but it comes with significant security risks. This article dives into the core issues of generative AI enterprise security, exploring common threats and offering best practices to protect your organization. Key Takeaways * Generative AI systems face unique security challenges, including risks of sensitive data exposure