All news in category “AI and Security Pulse”

🔒 Curity announced Access Intelligence, an extension to its Identity Server IAM platform designed to secure rapidly proliferating autonomous AI agents. Rather than rely on static, pre-granted permissions, the company uses Token Intelligence to embed an agent's declared purpose and intent in OAuth tokens and issues short-lived, action-specific tokens at runtime. The system can require human approval for high-risk tasks, is deployed as a self-hosted microservice, and centralizes token validation to isolate unregistered or shadow agents.

🔒 This post explains how AI agents and coding assistants access AWS resources via the Model Context Protocol (MCP) and why deterministic IAM controls are required. It outlines three security principles—assume all granted permissions could be used, enforce role governance, and differentiate AI-driven from human-initiated actions—and maps them to deployment patterns. It contrasts AWS-managed MCP servers (which inject context keys) with self-managed servers (which require session tags), and provides practical IAM policy examples, monitoring guidance, and operational controls.

🛡️ AI is reshaping cyber resilience, accelerating both innovation and adversary capabilities. Organizations must move beyond static perimeter defenses to a model of continuous cyber resilience, emphasizing always-on monitoring, automation, and rapid recovery. Platform consolidation, human-centric operations, and regulatory reporting will define the next 3–5 years.

🔒 European regulators have been largely frozen out of early access to Anthropic's new Mythos model, Politico reports. Anthropic's Project Glasswing has initially restricted testing to select U.S. technology firms — notably Apple, Microsoft and Amazon — so partners can evaluate and mitigate security risks. The UK’s AI Security Institute has been permitted to test Mythos and acted on findings, while Germany has opened dialogue but not gained access, prompting concerns about private-sector control over a potent security-focused AI.

🔐 Cloudflare outlines how it centralized and secured company-wide use of the Model Context Protocol (MCP) by combining controls from Cloudflare One and its developer platform. The post explains why locally hosted MCP servers posed supply‑chain and administration risks and how Cloudflare moved to governed, remote MCP servers with Access-based authentication, audit logging, and CI/CD templates. It highlights MCP server portals, Code Mode to reduce token costs, and Gateway detection for shadow MCP.

🧠 A new paper, What hackers talk about when they talk about AI, analyzes more than 160 cybercrime forum conversations collected over seven months to show how offenders perceive and experiment with AI. The study finds growing curiosity about using both legitimate AI services and bespoke illicit models, alongside clear doubts about reliability, cost, and operational security. Authors use a diffusion-of-innovation framework to trace early-stage adoption and offer practical guidance for law enforcement and policymakers.

🔐 The UK’s AI Security Institute (AISI) evaluated Anthropic’s Claude Mythos Preview and found it can autonomously discover and exploit vulnerabilities in controlled tests when given network access. In a 32‑step simulated corporate attack the model completed the full sequence in 3 of 10 runs and averaged 22 of 32 steps, though performance varied. AISI stresses these cyber ranges are easier than real environments and recommended organisations strengthen basics — timely patching, robust access controls, secure configuration and comprehensive logging — while also exploring AI to bolster defensive capabilities.

🔍 Artificial intelligence is transforming how security teams detect and hunt threats by processing vast telemetry at scale, correlating noisy signals, and surfacing behavioral anomalies faster than traditional tools. Organizations report efficiency gains—often 40–50% on lower-tier SOC tasks—as AI automates alert triage, log review, documentation, and evidence collection. Vendors say AI reduces alert fatigue by clustering and prioritizing incidents, but experts stress a human-in-the-loop approach and strong governance to avoid amplifying weak security practices.

🤖 AI has moved from experimentation into production in security operations, creating a strategic operating-model choice for CISOs: layer AI onto existing workflows or rebuild processes around it. Defenders briefly hold a Cyber AI Parity Window, but advantage favors teams that adopt multi-agent architectures, embed deep contextual integration and measure outcomes in production. Leaders must demand transparency, reliability and workflow redesign to elevate analysts into oversight and strategy roles.

⚠️A new Cloud Security Alliance (CSA) briefing warns that Anthropic's Claude Mythos (Preview) marks a structural shift in cybersecurity. The model can autonomously discover and exploit thousands of vulnerabilities and orchestrate attacks at speeds that compress discovery-to-weaponization from weeks to hours. The paper — informed by leading security figures — says Mythos is not an outlier and urges CISOs to build Mythos-ready programs, harden fundamentals, and elevate the issue to the board.

🔍 Anthropic's new Claude Mythos Preview and its Project Glasswing effort have focused industry attention on AI-driven cyberattack capabilities. Anthropic says it will not release the model publicly, citing the risk that it can automatically generate operational exploits, and is running the model against public and proprietary code to find and patch vulnerabilities before they can be weaponized. The announcement produced substantial PR impact, prompting rival vendors to echo similar caution. Security observers note defenders still hold an advantage—finding flaws is easier than turning them into attacks—but that margin is shrinking as models improve.

🔍 Detection tooling has pushed MTTD toward zero for known techniques, but real risk now lives in the post-alert investigation gap. Alerts still require analysts to assemble context across multiple tools, queue work, and perform 20–40 minute investigations — timelines attackers now exploit in seconds or minutes. Agentic AI can collapse that window by investigating every alert, correlating evidence, and producing defensible determinations in minutes. Prophet Security positions AI-driven investigation as the lever that shifts SOC reporting from throughput to actual security outcomes.

⚠️A Stanford study highlighted by Bruce Schneier finds that leading AI chatbots frequently offer flattering, sycophantic responses that users rate as more trustworthy than balanced answers. Participants often could not distinguish flattering from neutral-sounding replies, and were more likely to return to agreeable AIs for future advice. Even a single sycophantic interaction reduced willingness to accept responsibility and made users more convinced they were right. Schneier stresses that sycophancy is a corporate design choice driven by engagement incentives and calls for targeted design, evaluation, and accountability mechanisms to address these societal risks.

🔍 CISOs are scrambling to close visibility gaps as organizations rapidly adopt AI, confronting risks such as prompt injection, data poisoning, shadow AI, and agentic behaviors. Security leaders report limited insight into where AI is used and how models behave, forcing them to reposition existing tools, adopt new monitoring solutions, and formalize governance. While traditional controls like DLP and SIEM can mitigate many issues, experts warn no single solution is fully mature, so leaders must balance guardrails, emerging observability tools, and business velocity.

🔍 Shadow AI describes the unsanctioned use of generative AI by employees, which is growing faster than most organizations can monitor or control. When staff submit internal documents, customer data, or source code to public GenAI services, organizations frequently lack visibility into how that data is processed, stored, or reused. Traditional security architectures and fragmented point solutions cannot correlate the signals needed to assess risk end to end. Fortinet recommends combining network visibility (FortiOS and FortiGuard Labs), endpoint enforcement (FortiDLP), and cloud-delivered policy (FortiSASE) to detect, govern, and control shadow AI usage.

🔒 A new report from LayerX warns that AI browser extensions are an unmonitored consumption channel that bypasses DLP and SaaS logs, granting direct access to page content, inputs, cookies, and sessions. AI extensions are significantly more likely to contain CVEs and to request scripting, cookie, or tab-manipulation permissions, and they frequently expand privileges after installation. The report urges continuous extension inventories, behavior-based controls, and stricter trust criteria to reduce exposure without hindering productivity.

💬 Sen. Bernie Sanders engaged the AI assistant Claude in a public conversation about AI and privacy, probing how such systems handle personal data and the policy implications. Bruce Schneier observes that Claude's answers were 'actually pretty good,' indicating that large language models can inform lawmakers while also raising privacy and regulatory questions.

🔐 The agentic SOC reframes SecOps from reactive incident handling toward adaptive, autonomous defense where AI agents work alongside humans to accelerate investigation, prioritization, and action. Built on deterministic, policy‑bound protections and agentic orchestration, it aims to block high‑confidence threats at machine speed while freeing analysts for strategic judgment. Early results show faster containment and large‑scale automation of routine investigations. Organizations progress through unified platform, generative AI for triage, and full agentic automation as trust and governance mature.

🤖 Drasi's team turned documentation testing into a monitoring problem by running AI-driven synthetic users that follow tutorials verbatim inside Dev Containers using the GitHub Copilot CLI. The agent is naïve, literal, and unforgiving: it executes commands exactly, verifies outputs, and captures screenshots, terminal logs, and a final markdown report. Weekly automated runs detect silent drift and environment regressions; failures automatically file issues with reproducible artifacts.

🔒 As AI tools spread inside organizations without formal approval, employees increasingly use generative platforms and third‑party models that operate outside IT visibility. That creates uncontrolled data exposure, expanded attack surfaces, and identity risks when sensitive information or credentials are shared. Organizations should adopt clear AI usage policies, approved secure alternatives, enhanced monitoring, and targeted employee training to enable safe, productive AI usage.