All news with #ai security tag

🚀 The next generation of Amazon OpenSearch Serverless is now generally available, offering a fully managed search and vector engine optimized for agentic workflows. It auto-scales up to 20x faster and provisions resources in seconds, supports scale-to-zero and pay-per-usage pricing, and can reduce costs by up to 60% versus provisioning clusters for peak loads. New features include a shared storage layer that decouples compute and storage, two resource-based endpoints for simplified network connectivity, and native integrations with AI development platforms and OpenSearch Agent Skills.

🔒 India’s cybersecurity agency, CERT-In, has issued a framework urging organizations to patch, mitigate, or isolate known exploited internet-facing “crown jewel” systems within 12 hours where feasible, citing AI-assisted attacks that compress exploitation timelines. The 38-page blueprint prescribes tiered remediation windows—one day for externally exposed critical flaws, three days for critical internal issues, and five days for high-severity vulnerabilities—while emphasizing temporary mitigations and continuous exposure management over periodic assessments.

🔎 Adversarial AI has transformed targeted attacks into high-speed, automated campaigns that no longer require elite technical operators. Existing security architectures—fragmented, tool-heavy, and visibility-poor—fail to show defenders the chained attack paths attackers can exploit. The author argues for shifting from vulnerability counting to Exposure Management, prioritizing remediation by real exploitability and mapping environments as attacker-seen networks. Defenders retain an advantage if they synthesize cross-boundary telemetry and continuously assess validated attack paths to critical assets.

🔍 This Unit 42 report examines the growing shift from ransomware encryption to data-theft and extortion-only attacks, profiling threat actors, techniques, and sectors most affected. It highlights drivers such as improved backups, faster exfiltration, and regulatory pressures that make disclosure risk financially coercive. The briefing also warns of AI-accelerated attacks and offers prioritized defensive recommendations for DLP, SaaS posture, identity resilience, supply chain integrity, and AI preparedness.

🔒 Google announces AI Threat Defense, an integrated, automated security system that uses Gemini, Mandiant, Wiz, and CodeMender to detect, prioritize, and remediate AI-powered threats. The platform combines multi-model scanning, live exposure mapping, and AI agents to validate exploitability, generate fixes, and accelerate remediation. It emphasizes machine-speed monitoring, autonomous response, and consolidated visibility across development and runtime environments to reduce attack surface and speed patching.

🛡️ Recent developments show AI moving from automation to autonomous cyber operations, shifting how offense and defense interact. The Anthropic Mythos Preview and related incidents illustrate models discovering and chaining vulnerabilities with limited human direction, prompting coordinated defensive responses from major vendors. Policy and procurement are adapting, and security leaders must treat AI agents as principals, invest in adaptive defenses, and reframe risk models for continuous compromise.

🛡️ Microsoft warns of an active cryptojacking campaign that leverages AI chatbot interactions to surface malicious download sites. The attacks impersonate legitimate utilities and target high-performance GPU systems, using ZIP archives with sideloaded rogue DLLs to install ScreenConnect and deliver GPU miners. The campaign establishes persistent remote access, configures Defender exclusions, and supports multiple miners while evading analysis tools.

📡 Google details how its global network and new data center fabrics are being redesigned for AI workloads, describing a vertically integrated stack anchored by an AI Hypercomputer. The post highlights Virgo Network, campus-scale and WAN innovations, and AI-native Cloud Interconnect to meet extreme bandwidth, low latency, and burst tolerance requirements. It emphasizes co-design with accelerators, autonomous reliability features, and global footprint benefits for inference and cross-site training.

🛡️ This update describes Check Point’s Frontier AI Models Readiness Program and the resulting Jumbo Security Release. It outlines an AI-driven, multi-repository code-scanning initiative called BLAST that provides contextual, architecture-aware analysis to find exploitable vulnerabilities. The release includes dozens of hardening improvements and targeted fixes for multiple CVEs, and customers are urged to update to benefit from the protections.

🔒 The 2026 Cloud Security Report finds AI adoption has surged into production, but security architectures are lagging. While many organizations have updated strategies, few possess the architectural capability to enforce them, leaving AI systems and data exposed. The report calls for unified hybrid security architectures delivering consistent visibility, policy enforcement, and runtime controls across cloud, SaaS, and on-premises environments.

🔒 CERT-In has issued a 38‑page blueprint urging organisations to remediate known exploited, internet‑facing critical vulnerabilities within 12 hours where feasible to counter AI‑assisted automation of vulnerability discovery and exploitation. The guidance emphasizes continuous, risk‑based vulnerability and patch management, Zero Trust, defence‑in‑depth, supply chain scrutiny, and secure‑by‑design practices. It also prescribes tiered remediation timeframes for critical and high‑severity flaws and recommends temporary mitigations when patches are unavailable.

🛡️ Anthropic appears to be preparing a public rollout of its restricted Mythos model, which the company warned poses major security risks by automating high-quality cyberattacks. Announced in April as an advanced frontier model, Mythos showed dramatic improvements in code reasoning and autonomy compared to Opus 4.7. References briefly appeared in Claude Code and Claude Security, suggesting a controlled preview, while Anthropic builds guardrails and works with partners through its Glasswing initiative.

🛡️ Researchers argue enterprises must stop treating AI agents as trusted components and instead secure them as untrusted systems. The paper, authored by teams from Google, UC San Diego, UW–Madison and others, distills five systems-security principles—least privilege, tamper resistance, complete mediation, secure information flow, and human risk—and maps eleven real-world agent attacks to these violations. They caution that stacking ML guardrails is insufficient and propose research directions for separating instructions from data, verifiable least-privilege policies, and information-flow controls.

🛡️ The FBI has identified a new phishing-as-a-service platform called Kali365, first seen in April 2026, that is being distributed primarily via Telegram. The service furnishes AI-generated lures, automated templates and real-time tracking dashboards to enable attackers — including low-skill actors — to capture OAuth tokens and bypass MFA for Microsoft 365 accounts. Victims are tricked into pasting device codes into the legitimate Microsoft verification page, unintentionally authorizing attacker devices and granting persistent access to services such as Outlook, Teams and OneDrive. The FBI recommends restricting or blocking device code flow, implementing conditional access policies, blocking authentication transfer and protecting emergency access accounts.

🛡️ Check Point Research’s March–April 2026 Threat Landscape Digest documents that AI-powered attacks have moved from experimental and state-sponsored exercises into routine criminal deployment. The report details a campaign in Mexico where a single operator used commercial AI to compromise nine government agencies, leveraging persistent jailbreaks, weaponized agent configuration files, and commodified attack platforms like EvilTokens. It warns that stolen AI provider keys, rapid exploit timelines, and shadow AI use create urgent operational and supply-chain risks for organizations.

🔧 Industrial AI initiatives collide with legacy OT realities: an AI-ready control room can still depend on an unpatched Windows 7 maintenance laptop that alone communicates with protection relays. The author reports pervasive visibility gaps across utilities and plants, noting fewer than 10% of OT networks have meaningful monitoring. AI trained on IT telemetry misclassifies normal industrial traffic and automated responses risk shutting down production; passive monitoring of Level 0–2 protocols and a focus on crown-jewel processes are essential before layering AI.

🔒 Microsoft announced a set of security enhancements designed to protect agents, data, and identities as organizations scale AI. Highlights include the general availability of Microsoft Purview DSPM, expanded investigation capabilities with OCR and custom examinations, and a new Entra ID Account recovery flow for restoring organizational access. Public preview of Windows 365 for Agents and integration with Microsoft Agent 365 aim to govern and secure agent workloads in managed Cloud PCs.

🛡️ New studies reveal that 75% of organizations often or sometimes deploy code they know is vulnerable, down from 81% last year but still alarmingly high. Checkmarx warns that AI-augmented attackers are dramatically shortening time-to-exploit, while Verizon’s DBIR links increased initial access to vulnerability exploitation aided by AI. A QBE survey found UK firms are worried about suppliers' AI use, yet few audit third-party AI or maintain formal AI governance.

🛡️ Security professionals at DTX argued that integrating AI into SOCs is now essential to counter autonomous attacker tooling and AI-accelerated threats. Panelists stressed sustaining core cyberdefence fundamentals—system hardening, patching, access control and monitoring—before deploying AI, and preserving human oversight to manage model risk. They noted role shifts toward validation, prompt engineering and GRC, and urged rigorous testing and SDLC-like deployment controls.

🔒 AI security cannot be reduced to a single benchmark. Over the past 30 years software security evolved from black‑box penetration testing to white‑box analysis and process-driven standards such as BSIMM, and the report argues that AI requires a similar assurance-first approach. Benchmarks fail to capture emergent, systemic properties, so organizations should clean up their WHAT piles, adopt risk-based processes, and accept that there is no simple security meter for AI.