All news with #agent security tag

🚀 At GitHub Universe 2025, Microsoft and GitHub presented a vision for agentic development that lets developers see, steer, and build across autonomous agents. The event introduced platform capabilities like Agent HQ, a prompt-first AI Toolkit for VS Code, and the GA release of Azure MCP Server. Announcements focused on enterprise-grade security, standards-based integration, and faster, more intuitive agent creation and governance.

🔐 Autonomous AI agents are creating a new class of non-human identities that traditional, human-centric security models struggle to govern. These agents can persist beyond intended lifecycles, hold excessive permissions, and perform actions across systems without clear ownership, increasing risks like privilege escalation and large-scale data exfiltration. Security teams must adopt identity-first controls—unique managed identities, strict scoping, lifecycle management, and continuous auditing—to regain visibility and enforce least privilege.

🔐 Cloudflare presents a privacy-first approach to rate-limiting AI agents using anonymous credentials. The post explains how schemes such as ARC and ACT extend the Privacy Pass model by enabling late origin-binding, multi-show tokens, and stateful counters so origins can enforce limits or revoke abusive actors without identifying users. It outlines the cryptographic building blocks—algebraic MACs and zero-knowledge proofs—compares performance against Blind RSA and VOPRF, and demonstrates an MCP-integrated demo showing issuance and redemption flows for agent tooling.

🔐 Agentic AI is presented as a practical cybersecurity capability that can operate without direct human supervision, handling high-volume, time-sensitive tasks at machine speed. Industry leaders from Zoom to Dell Technologies and Deloitte highlight seven priority use cases — from autonomous threat detection and SOC augmentation to real-time zero‑trust enforcement — that capitalize on AI's scale and speed. The technology aims to reduce alert fatigue, accelerate mitigation, and free human teams for strategic work.

🔍 The episode of The Agent Factory reviewed practical AI agents for data engineering and data science, highlighting demos that combine Gemini, BigQuery, Colab Enterprise, and Spanner-based graph queries. It showcased a BigQuery Data Engineering Agent that generates pipelines, time dimensions, and data-quality assertions from SQL, and a Data Science Agent that runs end-to-end anomaly detection in Colab. The post also covered CodeMender for autonomous code security fixes and a creative Spanner+ADK comic demo illustrating multi-region concepts.

🔒 Prisma AIRS 2.0 is a unified AI security platform that delivers end-to-end visibility, risk assessment and automated defenses across agents, models and development pipelines. It consolidates Protect AI capabilities to provide posture and runtime protections for AI agents, model scanning and API-first controls for MLOps. The platform also offers continuous, autonomous red teaming and a managed MCP Server to embed threat detection into workflows.

🔒 Agentic AI introduces a mounting Zero Trust challenge as autonomous agents increasingly act with inherited or unmanaged credentials, creating orphaned identities and ungoverned access. Ido Shlomo of Token Security argues that identity must be the root of trust and recommends applying the NIST AI RMF through an identity-driven Zero Trust lens. Organizations should discover and inventory agents, assign unique managed identities and owners, enforce intent-based least privilege, and apply lifecycle controls, monitoring, and governance to restore auditability and accountability.

🔐 The Hacker News is promoting a free webinar that presents a practical framework to secure AI at scale while preserving speed of adoption. Organizers warn of a growing “quiet crisis”: rapid proliferation of unmanaged AI agents and identities that lack lifecycle controls. The session focuses on embedding security by design, governing AI agents that behave like users, and stopping credential sprawl and privilege abuse from Day One. It is aimed at engineers, architects, and CISOs seeking to move from reactive firefighting to proactive enablement.

🛡️ This recap of the Agent Factory episode explains practical strategies for securing production AI agents, demonstrating attacks like prompt injection, invisible Unicode exploits, and vector DB context poisoning. It highlights Model Armor for pre- and post-inference filtering, sandboxed execution, network isolation, observability, and tool safeguards via the Agent Development Kit (ADK). The team demonstrates a secured DevOps assistant that blocks data-exfiltration attempts while preserving intended functionality and provides operational guidance on multi-agent authentication, least-privilege IAM, and compliance-ready logging.

🤖 This post explains how System Integrator partners can build, scale, and manage enterprise-grade AI agents using Google Cloud technologies like Agent Engine, the Agent Development Kit (ADK), and Gemini Enterprise. It summarizes architecture patterns including runtime, memory, the Model Context Protocol (MCP), and the Agent-to-Agent (A2A) protocol, and contrasts managed Agent Engine with self-hosted options such as Cloud Run or GKE. Customer examples from Deloitte and Quantiphi illustrate supply chain and sales automation benefits. The guidance highlights security, observability, persistent memory, and model tuning for enterprise readiness.

📡 This recap of the Agent Factory podcast episode, hosted by Annie Wang with guest Ivan Nardini, explains how to evaluate autonomous agents using a practical, full-stack approach. It outlines what to measure — final outcomes, chain-of-thought, tool use, and memory — and contrasts measurement techniques: ground truth, LLM-as-a-judge, and human review. The post demonstrates a 5-step debugging loop using the Agent Development Kit (ADK) and describes how to scale evaluation to production with Vertex AI.

⚙️ Microsoft is introducing Copilot Actions, a Windows 11 Copilot feature that allows AI agents to operate on local files and applications by clicking, typing, scrolling and using vision and advanced reasoning to complete multi-step tasks. The capability will roll out to Windows Insiders in Copilot Labs, extending earlier web-based actions introduced in May. Agents run in isolated Agent Workspaces tied to standard Windows accounts, are cryptographically signed, and the feature is off by default.

🛡️ Microsoft released ExCyTIn-Bench, an open-source benchmarking tool to evaluate how well AI systems perform realistic cybersecurity investigations. It simulates a multistage Azure SOC using 57 Microsoft Sentinel log tables and measures multistep reasoning, tool usage, and evidence synthesis. The benchmark offers fine-grained, actionable metrics for CISOs, product owners, and researchers.

🤖 Organizations are rapidly adopting agentic AI that does more than suggest actions—it opens tickets, calls APIs, and even remediates incidents autonomously. These agents differ from traditional Non-Human Identities because they reason, chain steps, and adapt across systems, making attribution and oversight harder. The author from Token Security recommends named ownership, on‑behalf tracing, and conservative, time‑limited permissions to curb shadow AI risks.

🔒 A new SquareX Labs report warns that integrating AI assistants into browsers—exemplified by Perplexity’s Comet—introduces architectural security gaps that can enable phishing, prompt injection, malicious downloads and misuse of trusted apps. The researchers flag risks from autonomous agent behavior and limited visibility in SASE and EDR tools. They recommend agentic identity, in-browser DLP, client-side file scanning and extension risk assessments, and urge collaboration among browser vendors, enterprises and security vendors to build protections into these platforms.

⚠️ AI agents are increasingly automating cyberattacks, performing reconnaissance, exploitation, and data theft at machine speed and scale. In 2023 examples include XBOW's mass vulnerability reports, DARPA teams finding dozens of flaws in hours, and reports of adversaries using Claude and HexStrike-AI to orchestrate ransomware and persistent intrusions. This shift threatens accelerated attacks beyond traditional patch cycles while presenting new defensive opportunities such as AI-assisted vulnerability discovery, VulnOps, and even self-healing networks.

🤖 Google Cloud published the Startup technical guide: AI agents, a practical, operations-driven roadmap to design, build, and operate agentic systems for startups. The guide outlines three paths — build with the open-source Agent Development Kit (ADK), design no-code agents in Agentspace, or adopt managed and partner agents via Vertex AI and the Agent Garden marketplace. It details four development steps (identity, prime directive, tools, lifecycle), highlights operational rigor (AgentOps), and promotes interoperability through standards such as MCP and A2A, all aimed at safe production deployment.

🔒 Google announced a set of AI security measures: the AI-powered agent CodeMender, a dedicated AI Vulnerability Reward Program (AI VRP), and Secure AI Framework 2.0 (SAIF 2.0). CodeMender leverages advanced reasoning to find, self-validate, and propose patches at scale. SAIF 2.0 introduces an agent risk map and secure-by-design controls, while the AI VRP centralizes reporting and incentives to accelerate remediation.

🔒 Databricks has introduced Data Intelligence for Cybersecurity, an AI-driven platform that unifies fragmented security telemetry on its Lakehouse architecture to provide real-time, context-rich threat detection. The offering includes Agent Bricks to build governed AI agents, conversational dashboards, and natural-language queries for nontechnical stakeholders. Early adopters such as Arctic Wolf, Palo Alto Networks, and SAP report sharper detection, lower costs, and faster operations, while Databricks expands integrations across a broad partner ecosystem to challenge established SIEM and analytics vendors.

🛡️ Agentic AI is moving from lab experiments into real-world SOC deployments, where autonomous agents triage alerts, correlate signals across tools, enrich context, and in some cases enact first-line containment. Early adopters report fewer mundane tasks for analysts, faster initial response, and reduced alert fatigue, while noting limits around noisy data, false positives, and opaque reasoning. Most teams begin with bolt-on integrations into existing SIEM/SOAR pipelines to minimize disruption, treating standalone orchestration as a second-phase maturity step.