Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI models are becoming essential to enterprise innovation, but the sensitive data that powers them is creating new security and privacy challenges. Even when raw training datasets remain inaccessible, attackers may still identify whether specific information was used to train a model through membership inference attacks.

A privacy policy that only works in English is not a global privacy policy. It is an English-language policy that a global company happens to be using. That distinction matters more than most teams realize. Enterprises now centralize contracts, HR files, healthcare records, and support conversations from regional offices around the world into a shared AI platform, often assuming that whatever detection and masking logic works for their English-language content will work everywhere else. It does not.

• Zenity secures ChatGPT Workspace Agents across their full lifecycle, from posture management at build time to detection and response at runtime. • AgentForger showed how a single link could forge an autonomous AI agent that inherits a real employee's identity and access, a risk legacy security tools can't see. • Zenity's AISPM catches the misconfigurations these attacks rely on, such as agents that auto-approve sensitive actions or connect to privileged systems.

When Hugging Face's security team sat down to reconstruct what had torn through their production infrastructure in mid-July, they had more than 17,000 recorded attacker actions to sort through, spread across a swarm of short-lived sandboxes with decoy activity planted to slow them down. They did what any competent team would do in 2026 and reached for a frontier model to help triage the logs. However, the commercial APIs refused.

The constant evolution of today's threat landscape has organizations counting on security controls to keep the bad actors out and safeguard their people, sensitive data, critical infrastructure, operations, and brand. However, even the most sophisticated security tools can present a risk when improperly configured. And unfortunately, even the best security teams can make mistakes.

Managing a WordPress site no longer means logging in to the dashboard for every update or routine task. With the Model Context Protocol (MCP), AI assistants such as ChatGPT, Claude, and Cursor can securely interact with your WordPress site through natural language. They can retrieve content, update posts, manage WooCommerce stores, and perform other actions without custom integrations.

Every conversation our team has with security leaders begins the same way. Nobody is backing off on AI in the SOC. The direction of the lean is what’s shifting. Torq’s 2026 AI SOC Leadership Report surveyed more than 450 CISOs and SOC leaders. The data confirms what those conversations were already telling me. We’ve left the adoption phase. The market is now in the architecture phase and the implications for how teams plan, buy, and build are significant.

The enterprise AI security market in 2026 is crowded and confusing. Vendors that built their products to use AI for cybersecurity operations now market themselves alongside vendors that built their products to secure AI systems and govern AI usage. These are fundamentally different product categories solving different problems, and conflating them leads to evaluation errors that leave organizations protected against external threats but exposed to the risks their own AI systems introduce. ‍