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FIRST LOOK Microsoft Copilot Gains Local File Access via Hybrid Intelligence // FIRST LOOK AWS Adds Native Access Controls for RAG via Amazon Quick and Bedrock // HIGH DPRK npm Supply Chain Worm Uses Web3 C2 to Steal Cloud Keys // FIRST LOOK Rein Security Launches $25M Runtime Guard for AI Agents // HIGH APT Uses AI-Generated Lures in Google AitM Phishing on Taiwan // CRITICAL Tensorlake npm Package Hijacked by Shai-Hulud Worm // FIRST LOOK Meta Launches Open Standard to Authenticate AI Agents on the Web // FIRST LOOK Anthropic Launches 3-Tier Cyber Verification Program for AI Access // FIRST LOOK CrowdStrike Maps LLM Safety Classifier Evasion for Defenders // HIGH OpenAI Adds Training Monitors After Medicare Data Breach //
ENFORCEMENT

Enforcement

2 reports
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Kontext Security Launches AI Agent Runtime Enforcement Platform

Kontext Security Launches AI Agent Runtime Enforcement Platform

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 SecurityWeek

Kontext Security has emerged from stealth with $4 million in funding and a runtime enforcement platform that evaluates AI agent actions in real time, providing visibility and control over what agents do during execution. This directly addresses one of the most pressing gaps in agentic AI security: the absence of continuous, in-flight oversight of agent behaviour beyond static policy definitions. The platform's maturity and integration breadth across diverse agent frameworks and enterprise environments will determine how broadly defenders can realise its promise.

AI Agent Security Shifts From Visibility to Enforcement Controls

AI Agent Security Shifts From Visibility to Enforcement Controls

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.8 The Hacker News

Security practitioners are documenting a critical maturity gap in AI agent governance: organisations can now inventory deployed agents across SaaS, cloud, and developer environments, but lack enforcement mechanisms to constrain what those agents can actually do. The core risk is that AI agents operate without consistent identity, intent, ownership, or access boundaries, breaking every assumption that traditional IAM and least-privilege models rely on. Defenders must treat agent enforcement — not discovery — as the primary control objective, or risk a false sense of security from visibility tooling alone.

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