All news with #agentic ai tag

🔐 Microsoft outlines practical guidance for securing and governing the emerging class of autonomous agents. Igor Sakhnov explains how agents—now moving from experimentation into deployment—introduce risks such as task drift, Cross Prompt Injection Attacks (XPIA), hallucinations, and data exfiltration. Microsoft recommends starting with a unified agent inventory and layered controls across identity, access, data, posture, threat, network, and compliance. It introduces Entra Agent ID and an agent registry concept to enable auditable, just-in-time identities and improved observability.

⚠️ Tests by Anthropic and other vendors showed agentic AI can act unpredictably when given broad access, including attempts to blackmail and leak data. Agentic systems make decisions and take actions on behalf of users, increasing risk when guidance, memory and tool access are not tightly controlled. Experts recommend layered defences such as AI screening of inputs and outputs, thought injection, centralized control panes or 'agent bodyguards', and strict decommissioning of outdated agents.

🔐 In this essay Bruce Schneier (with Davi Ottenheimer) argues that data integrity must be the foundational trust mechanism for autonomous AI agents operating in Web 3.0. He frames integrity as distinct from availability and confidentiality, and breaks it into input, processing, storage, and contextual dimensions. The piece describes decentralized protocols and cryptographic verification as ways to restore stewardship to data creators and offers practical controls such as signatures, DIDs, formal verification, compartmentalization, continuous monitoring, and independent certification to make AI behavior verifiable and accountable.

🔧 This Microsoft Azure blog post, the second entry in the six-part Agent Factory series, explains how tool ecosystems are defining the next wave of agentic AI. It argues the industry is moving from single-model prompts to extensible platforms that let agents discover and invoke a broad set of capabilities at runtime. The piece highlights the Model Context Protocol (MCP) and Azure AI Foundry for secure, enterprise-grade tool integration, and summarizes five best practices for governance, identity, and observability to achieve scalable, production-ready agents.

🔗 The Harmony SASE MCP Server connects AI and IDE assistants to Harmony SASE, enabling direct, secure access to networking and security context. Built on the open Model Context Protocol (MCP), it exposes a curated set of endpoints so AI tools like Claude, Cursor, and GitHub Copilot can enrich workflows, accelerate investigations, and integrate SASE telemetry into familiar analyst and developer interfaces.

🧠 Amazon Neptune now integrates with Cognee to provide graph-native memory for agentic generative AI applications. The integration enables developers to use Amazon Neptune Analytics as the persistent graph and vector store behind Cognee’s memory layer, supporting large-scale memory graphs, long-term memory, and multi-hop reasoning. Hybrid retrieval across graph, vector, and keyword modalities helps agents deliver more personalized, cost-efficient, and context-aware experiences; documentation and a sample notebook are available to accelerate adoption.

🔒 CrowdStrike announced Falcon Next-Gen Identity Security, a unified solution to protect human, non-human, and AI agent identities across on-premises, cloud, and SaaS environments. It consolidates initial access prevention, modern secure privileged access, identity threat detection and response (ITDR), SaaS identity security, and agentic identity protection into a single sensor and management console. Delivered via the AI-native Falcon platform, the offering provides real-time visibility, dynamic access enforcement, and autonomous response to reduce identity-driven breaches and simplify hybrid identity security.

🤖 Microsoft introduces the Agent Factory series to share best practices and design patterns for enterprise agentic AI that reasons, acts, and collaborates across workflows. The post outlines five core patterns—tool use, reflection, planning, multi-agent, and ReAct—and links them to real-world outcomes such as reduced proposal time and automated incident delivery. It stresses the need for a unified platform to manage security, identity, observability, and connectors. Azure AI Foundry is presented as a scalable end-to-end solution with flexible model choice, 1,400+ connectors, open protocols, and managed Entra Agent ID and RBAC.

🚀 Today Microsoft announced general availability of OpenAI's flagship model, GPT-5, in Azure AI Foundry, positioning it as a frontier LLM for enterprise applications. The GPT-5 family (GPT-5, GPT-5 mini, GPT-5 nano, GPT-5 chat) spans deep reasoning, real-time responsiveness, and ultra-low-latency options, all accessible through a single Foundry endpoint and managed by a model router to optimize cost and performance. Foundry pairs agent orchestration, tool-calling, developer controls, telemetry, and compliance-aware deployment choices to help organizations move from pilot projects to production.