All news with #ai security tag

🚀 At KubeCon + CloudNativeCon Europe 2026, Microsoft outlined coordinated open-source and AKS enhancements designed to bring AI workloads to Kubernetes with enterprise-grade operational patterns. Upstream work includes DRA reaching GA, Workload Aware Scheduling for Kubernetes 1.36, DRANet Azure RDMA compatibility, and new projects such as AI Runway, HolmesGPT (CNCF Sandbox), and Dalec. AKS platform updates add identity-aware networking with Azure Kubernetes Application Network, meshless Istio routing, WireGuard and Cilium-based encryption, built-in GPU telemetry into managed Prometheus/Grafana, per-flow L3/L4 and L7 observability, a managed Cilium cluster mesh via Fleet Manager, Elastic SAN shared storage, AKS Desktop GA, and safer upgrades with blue-green agent pool upgrades and agent pool rollback.

🛡️ AI-driven honeypots pair large language models with deception servers to create dynamic, realistic environments that keep attackers engaged longer and collect richer threat intelligence. Academic research by Dr. M. Abdullah Canbaz and others showed LLMs can parse traffic and handle complex Linux commands, prompting open-source and commercial efforts such as Beelzebub and Deutsche Telekom’s T-Pot. These systems significantly lower the cost and engineering effort of high-interaction deception while enabling deployment in novel locations like APIs and AI agents. However, defenders must balance benefits with risks—attackers are using AI to automate attacks and may develop countermeasures such as deception-detection services or data poisoning—so CISOs should view AI honeypots as a complement to existing sensors and an important tool for improved visibility and hunting.

🔒 Palo Alto Networks updated Prisma AIRS and its Prisma Browser to discover and map AI agents, models and connections across cloud, SaaS and endpoints, scan agent artifacts for vulnerabilities, and simulate agent-targeted attacks. Prisma AIRS 3.0 — contingent on the planned acquisition of Koi Security — will add an AI Agent Gateway to enforce agent runtime and identity security. Prisma Browser now detects user-generated AI activity, enforces content-aware boundaries, prevents sensitive data from leaking to unmanaged LLMs, and blocks prompt-injection attacks. Separately, following its CyberArk deal, Palo Alto introduced Next Generation Trust Security (NGTS) to automate certificate discovery and lifecycle management.

🔒 Palo Alto Networks announces the next evolution of Prisma SASE, engineered to secure the emerging era of agentic AI by treating autonomous agents as first-class identities. The platform reimagines Prisma Browser as a secure AI workspace, extending AI-powered data protection across endpoints, network, SaaS and GenAI apps while detecting prompt injection and agent hijacking. It also adds autonomous operations and resilient deployment options, including SASE Private Location and hyperscaler integration, to ensure always-on performance for machine-speed workflows.

🔒 Varonis today announced general availability of Varonis Atlas, an end-to-end AI security platform that discovers, assesses, tests, and enforces controls across AI systems and the data they access. The platform integrates AI inventory, AI-SPM, pentesting, runtime guardrails, monitoring, AIDR, and third-party risk into a single solution built on the Varonis Data Security Platform. Atlas emphasizes data-aware security, customer-owned telemetry, and compliance reporting to help enterprises govern AI at scale.

🧠 This article presents a blueprint architecture for securing AI data centers and AI factories as enterprises shift from consuming AI to producing it. It explains how organizations can protect LLMs, data pipelines, and compute infrastructure against emerging, AI-specific threats by combining network segmentation, identity and access controls, data governance, and advanced threat prevention. Check Point emphasizes operational practices and industrial-grade security controls to enable secure, revenue-generating AI deployments.

🔒 At RSAC, Check Point outlined how enterprises can secure their AI transformation by protecting sensitive data, models, and communications from emerging attack vectors. The company emphasized integrated controls—data loss prevention, access governance, model protection and runtime threat detection—paired with unified visibility across cloud and on‑prem environments. Check Point shared practical steps for safe AI adoption, from discovery and policy enforcement to automated response, helping organizations reduce leakage and compliance risk while enabling productive AI use.

🔒 XM Cyber researchers identified eight validated attack vectors inside AWS Bedrock, showing that integrations and permissions — not the foundation models themselves — are the primary risk. The team highlights log manipulation, knowledge base compromise, agent hijacking, flow injection, guardrail degradation, and prompt poisoning as practical paths to data exfiltration and operational abuse. Their findings show how a single over-privileged identity can redirect logs, steal credentials, or subvert agents and prompts. Security teams should inventory AI workloads, enforce least privilege, and map cross-environment attack paths to reduce exposure.

🔒 CrowdStrike announced a suite of innovations that expand AI detection and response across endpoints, SaaS, and cloud environments. New capabilities include runtime monitoring for desktop AI applications and Copilot Studio agents, unified discovery and classification of AI agents across SaaS, and data-flow visibility for cloud-hosted AI workloads. Several features are in pre-beta or early beta with staged GA rollouts planned over upcoming quarters.

🛡️ AI-enabled cyber attacks increasingly mimic legitimate users, rendering signature- and rule-based defenses insufficient. Modern identity security must adopt continuous, context-aware risk modeling that evaluates identity, device and session context in real time to detect subtle deviations. Organizations should extend monitoring across cloud, endpoints and privileged accounts, enforce Just-in-Time (JIT) access and consolidate behavioral analytics with session monitoring and granular controls to limit credential abuse and insider misuse.

🔒 Microsoft announced Zero Trust for AI, extending proven Zero Trust principles across the AI lifecycle and shipping new tools and guidance to help security teams deploy AI with confidence. The update adds an AI pillar to the Zero Trust Workshop, expands the Zero Trust Assessment to include Data and Networking, and introduces a Zero Trust for AI reference architecture. Microsoft also published practical patterns for threat modeling and AI observability to help teams verify agents, apply least privilege, and assume breach.

🤖 Between January and February 2026, AI-assisted malware development matured from experimentation into operational capabilities that materially change attack economics. What once required coordinated teams can now be executed by a single experienced developer using an AI-powered IDE, accelerating weaponization, iteration, and delivery of attacks. Enterprise productivity and development tools have become enlarged attack surfaces, while automation and agentic workflows enable faster, more evasive intrusion chains. Defenders must shift toward behavior-based detection, robust telemetry, and secure development and supply chain controls.

⚠️ Unit 42 examines real-world instances where malware calls external LLMs for decision making or cosmetic effect. The researchers present two representative cases: a trio of obfuscated .NET infostealers that call OpenAI GPT-3.5-Turbo but largely perform "AI theater" by logging model outputs without functional integration, and a Go dropper that queries GPT-4 to gate Sliver payload execution. The report highlights detection opportunities and recommends Advanced Threat Prevention, Advanced WildFire, and Cortex XDR/XSIAM to monitor telemetry and IOCs.

🤖RSA Conference 2026 reframes AI from a single track to the event itself, with roughly 40% of sessions AI-weighted and artificial intelligence woven across identity, cloud, threat intelligence and human-focused tracks. CISOs face a dual mandate: accelerate AI adoption to remain competitive while protecting the enterprise from new attack surfaces such as RAG pipelines, vector databases, prompt injection and model inversion. Key priorities at RSAC include securing the AI stack, defining AI governance and compliance (including preparation for the EU AI Act), managing non‑human identities, mitigating shadow AI and AI-assisted coding risks, and preparing SOCs for autonomous remediation.

🛡️ Gartner warns custom-built AI applications will increasingly strain security teams unless defenders are engaged early. It predicts that by 2028 at least half of enterprise incident response work will handle fallout from AI app security issues. Analysts urge teams to "shift left" to embed controls during development, and expect AI security platforms to be widely adopted within two years to enforce guardrails and mitigate prompt injection, data misuse and related threats.

🔐 CISOs are updating data protection strategies as employees rapidly adopt AI tools that access and expose sensitive information. Leaders such as Scott Kopcha at Goodwin Procter and experts from SANS and Health-ISAC warn that traditional controls and many DLP tools are insufficient for the multiple ways AI can interact with data. Organizations are prioritizing data classification, identity and access management, continual monitoring, zero-trust, and ongoing vendor evaluations to close gaps and show due diligence.

🔒Google and industry partners are committing $12.5 million through the Linux Foundation's Alpha-Omega Project and OpenSSF to strengthen open source security for the AI era. The funding targets maintainer support, moving beyond vulnerability discovery to accelerated deployment of fixes and equipping projects with advanced AI-driven tooling to triage and remediate AI-generated findings. Google highlights internal tools such as Big Sleep and CodeMender, and research like Sec-Gemini, as examples of AI that can autonomously find and fix deep vulnerabilities.

🔍 LayerX researchers demonstrated a font-rendering technique that can hide malicious commands from AI assistants by encoding the payload in HTML while visually rendering a different, benign string to users. The proof-of-concept combines custom fonts with glyph substitution and CSS concealment (tiny fonts, color/opacity tricks) so the DOM appears harmless while the browser displays an executable instruction. In tests across many popular assistants, automated analyzers that read the DOM missed the hidden commands; LayerX urges assistants to compare rendered output with DOM text and to treat fonts, color/opacity matches, and unusually small fonts as potential attack surfaces.

🔒 The Pentera AI and Adversarial Testing Benchmark Report 2026, based on a survey of 300 US CISOs and senior security leaders, finds that most security teams lack the tools and skills to secure AI systems. 67% of respondents report limited visibility into AI usage, while half cite a lack of internal expertise. Organizations largely extend legacy security controls—75%—and only 11% use AI-specific tools.

🤖 Insurers are reshaping cyber policies as AI proliferates in business operations. Many carriers are tightening language, adding exclusions, and requiring evidence of active controls rather than relying on checkbox attestations. At the same time, firms that deploy AI-driven defenses and continuous monitoring can receive premium discounts. Brokers and policyholders must clarify AI usage and coverage before renewals to avoid gaps.