Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

How CISOs Should Brief the Board on AI in the SOC

John White is the Field CISO for EMEA at Torq. A respected security executive with more than 20 years of leadership experience, John previously served as CISO at Virgin Atlantic, where he led a multi-year transformation deploying the Torq AI SOC Platform to modernize cyber operations. Prior to that, he built and transformed security functions for global organizations, including ASOS, Liberty Global, AEG Europe, and KPMG.

How Is AI Transforming Identity and Access Management?

Traditional Identity and Access Management (IAM) relies on authentication methods, predefined roles, and access policies to control who can access business applications and data. These controls remain essential, but they do not always provide enough context to identify unusual behavior or changing access risks. AI in Identity and Access Management (IAM) adds another layer of intelligence by analyzing login context, user behavior, device information, access patterns, and identity activity.

An agent breaks in production. Who's accountable?

We asked eight security and product leaders who's accountable when an agent ships to production and breaks something. Nobody said the model. Harish Gaggar named the reason. An agent runs on permissions someone approved and configuration someone set. Ron Reiter drew the line in the same place, accountability sits with whoever decided what the agent could actually do. As agents act across more systems, the accountability trail gets harder to follow. Most teams cannot determine which human granted an agent access.

AI agents can inherit local admin rights

An AI agent runs as a process under whatever account launched it, and it inherits that account's access token. If the account has local admin rights, so does the agent, along with every helper process and script it spawns, an example would be Claude Desktop running under an admin account, spawning PowerShell helpers. What makes agents different from a typical privileged app is that their next action often comes from content parsed at runtime, including untrusted input.

A Complete Audit Trail That Names No One

An AI assistant reads four hundred documents across a tenant. Every read is logged. The application is named, the file is named, the timestamp is exact, and the access is attributed to an account that belongs to nobody. ‍ The audit trail is complete and it cannot answer the question an auditor asks. Nobody asks whether an access was recorded. They ask who reached the data and whether that person was authorized, and a shared service account answers neither. ‍

OWASP Top 10 for Large Language Model Applications: Complete Guide to LLM Security Risks

Companies rush to utilise the potential of large language models; however, every new use case of generative AI introduces attack vectors previously unknown in traditional web security. The present guide provides an overview of the official OWASP GenAI LLM Top 10 2026 list and explains the appearance of each vulnerability in practice along with mitigation recommendations.

Best Business Management Software in 2026: 10 Options Compared by Category

Every growing company eventually hits the same wall: the tools that worked at 10 people stop working at 50. Spreadsheets turn into a guessing game about which version is current. Goals live in a slide deck nobody opens after the kickoff. Nobody can say for sure who owns a given process anymore. The fix isn't one universal tool - it's picking the right category for the problem you actually have. Here's how the ten most-recommended options break down.

Why DevSecOps Teams Are Adopting an AI Pentesting Solution

Software teams today are shipping code faster than ever before. New features go live weekly, sometimes daily, and the pressure to stay ahead of competitors means security can no longer be treated as a final checkpoint before release. This shift has pushed DevSecOps teams to rethink how they test for vulnerabilities.