All news with #ai governance tag

🔍 AI is reshaping cybersecurity: while models speed detection and automate response, human judgment and communication are the differentiators. CISOs must cultivate data fluency, risk literacy, executive communication, cross-functional collaboration and ethical foresight. Practical steps include regular AI bias audits, joint security/data-science sprints and measuring data-storytelling maturity to align AI with business risk.

⚖️ The essay opposes a proposed ten‑year moratorium and an impending Executive Order that would bar states from regulating artificial intelligence, arguing this would cede power to a few dominant AI firms and undermine local consumer protections. It highlights growing state efforts in places like California, New York, Massachusetts, Utah, and Texas and rejects the industry claim that a regulatory patchwork would fatally stifle innovation. The authors advocate that the federal government should support state-led experimentation and fund public-interest AI models rather than preempt state authority, and note that the President signed an Executive Order shortly after publication.

🔹Google's Data Cloud presents an AI-native approach to unify siloed, multimodal enterprise data and enable real-time insights. The platform integrates Gemini with BigQuery to automate pipeline building, vector embedding, and data quality, while Dataplex provides continuous cataloging and contextual metadata for agents. Enhancements to managed streaming, Pub/Sub UDFs, and Dataflow extend real-time processing, governance, and developer productivity.

🔐 Bruce Schneier warns that personal AI assistants cannot be trusted without robust integrity controls, arguing that current systems routinely push users toward bad outcomes, gaslight them, and mishandle personal context. He urges decoupling personal data stores from AI models so that cryptographic verification, access control, and auditable logs can be developed independently of model performance. Such stores should be interoperable with many models, provably accurate, under fine‑grained user control, resilient to read and write attacks, and easy to use; Schneier cites Inrupt work extending Solid and the Human Context Protocol as practical directions.

🔍 IDC warns of a growing Total Cost of Ownership (TCO) crisis as AI inference becomes the dominant workload. Their global survey of 1,300 AI decision-makers finds inference already accounts for 47% of AI operations and is magnified by agentic workflows that trigger many sequential model calls. The research attributes the problem to fragmented stacks and idle accelerators and recommends shifting to integrated, system-level architectures that unite software, storage, networking, and compute. Google Cloud highlights AI Hypercomputer as a purpose-built solution to improve utilization and cost-effectiveness.

🔒 Microsoft argues that as organizations adopt agentic AI, security must be a strategic priority that enables growth, trust, and continued innovation. The post identifies risks such as oversharing, data leakage, compliance gaps, and agent sprawl, and recommends three pillars: prepare for AI and agent integration, strengthen organization-wide skilling, and foster a security-first culture. It points to resources like Microsoft’s AI adoption model, Microsoft Learn, and the AI Skills Navigator to help operationalize these steps.

🤖 The 2025 DORA companion guide highlights that AI acts as an amplifier, boosting strengths and exposing weaknesses across teams. Drawing on a cluster analysis of nearly 5,000 technology professionals, it identifies seven foundational capabilities — including a clear AI stance, healthy and AI-accessible data, strong version control, small-batch workflows, user-centric focus, and quality internal platforms — that increase the odds of positive outcomes. The guide maps seven team archetypes to help leaders diagnose where to start and offers a Value Stream Mapping facilitation to direct efforts toward system-level constraints so AI-driven productivity scales safely.

🔒 Gartner and Palo Alto Networks argue that AI security must be treated as a platform problem to manage accelerating generative AI risk, cost and complexity. The post recommends a two‑phase path: start with AI usage control to govern third‑party GenAI consumption, then extend protections into AI application development and runtime. Prisma Browser, Prisma SASE and Prisma AIRS are presented as the integrated tooling to discover, govern and protect AI usage and models. Palo Alto highlights Unit 42, Huntr and autonomous red teaming as sources of continuous validation.

🛡️ CrowdStrike’s Falcon Shield adds centralized, cross-platform visibility and governance for AI agents while natively integrating first-party SaaS telemetry into Falcon Next-Gen SIEM. The update automatically inventories and classifies agents, maps privileges to human and service identities, and detects risky configurations and agent-to-agent misuse. Teams can alert or suspend agents and associated accounts through Falcon Fusion SOAR, applying human identity controls to AI-driven automation.

🛡️ Organizations must build AI governance now to avoid repeating past technical debt. The article warns that rapid AI adoption mirrors earlier waves — cloud, IoT and big data — where innovation outpaced oversight and created security, privacy and compliance gaps. It prescribes pragmatic controls like classification and ownership, baseline cybersecurity, continuous monitoring, third‑party due diligence and regular testing. The piece also highlights the accountability vacuum from agent AIs and urges business‑led governance and clear executive responsibility.

🔒 Zero trust is over 15 years old, yet many organizations continue to struggle with implementation due to legacy systems, fragmented identity tooling, and cultural resistance. Experts advise shifting segmentation from devices and subnets to applications and identity, adopting pragmatic, risk-based roadmaps, and prioritizing education to change behaviors. As AI agents proliferate, leaders must extend zero trust to govern models and agent identities to prevent misuse while using AI to accelerate policy definition and threat detection.

⚠️ The NSA, together with the Australian Signals Directorate and allied security agencies, published the Principles for the Secure Integration of Artificial Intelligence in Operational Technology to highlight emerging risks as AI is applied to safety-critical OT networks. The guidance flags adversarial prompt injection, data poisoning, AI drift, hallucinations, loss of explainability, human de-skilling and alert fatigue as primary concerns. It urges operators to adopt CISA secure design practices, maintain accurate asset inventories, consider in-house development tradeoffs, and apply rigorous oversight before deploying AI in OT environments.

🔒 Microsoft is expanding Azure with on‑premises, edge, and hybrid options to deliver AI, resilience, and operational sovereignty. Azure Local provides integrated compute, storage, and networking on customer premises with GA features like Microsoft 365 Local and NVIDIA Blackwell GPUs, plus previews for disconnected operations and multi‑rack scale. Coupled with Azure IoT, Microsoft Fabric, and Azure Arc management enhancements, the updates enable near‑real‑time analytics, secure device identity, and a unified control plane for distributed estates. The goal is to accelerate AI and analytics while preserving data residency, continuity, and compliance for regulated or mission‑critical environments.

🧭 Google announces generally available automated metadata generation in the Google Data Cloud, using Dataplex Universal Catalog and Gemini to convert profiling and schema context into human-readable table and column descriptions. The capability integrates with BigQuery, stores generated descriptions for search and governance, and is accessible via an API. It aims to reduce "metadata debt," accelerate time-to-insight, and provide reliable grounding for AI agents, while still encouraging human review for key business definitions.

🔒 The Cybersecurity and Infrastructure Security Agency (CISA) and the Australian Signals Directorate’s Australian Cyber Security Centre published a joint guide outlining four principles to safely integrate AI into operational technology (OT). The guidance emphasizes educating personnel, assessing AI uses and data risks, establishing governance, and embedding safety and security. It focuses on ML, LLMs, and AI agents while remaining applicable to other automation approaches. CISA and international partners encourage OT owners and operators to adopt these risk-informed practices to protect critical infrastructure.

🔒 CISA and the Australian Signals Directorate released joint guidance, Principles for the Secure Integration of Artificial Intelligence in Operational Technology, to help critical infrastructure owners and operators balance AI benefits with OT safety and reliability. The guidance focuses on ML, LLMs, and AI agents while remaining applicable to traditional statistical and logic-based systems. It emphasizes four core areas—Understand AI, Assess AI Use in OT, Establish AI Governance, and Embed Safety and Security—and recommends integrating AI considerations into incident response and compliance activities.

🪓 Security teams face a pivotal moment as AI becomes embedded across products while core decision-making remains opaque and vendor‑controlled. The author urges building and tuning small, controlled AI‑assisted utilities so teams can define training data, risk criteria, and behavior rather than blindly trusting proprietary models. Practical skills — basic Python, ML literacy, and active model engagement — are framed as essential. The piece concludes with an invitation to a SANS 2026 keynote for deeper, actionable guidance.

🏆 The 2025 CSO 30 Awards celebrate cybersecurity leaders blending technology, culture and measurable impact. A panel of judges recognised achievements across categories such as AI and Digital Excellence, Rising Star, Diversity and Inclusion and CSO of the Year. Highlights include Greg Emmerson’s automation and canary tooling at Applegreen, Chris Bardell’s response advances at Royal Papworth Hospital, and Craig Hickmott’s human-first transformation at the British Heart Foundation. The programme emphasises workforce development, responsible AI and organisational resilience.

🧭 The piece argues that AI forces a societal reckoning similar to the arrival of social media: it can amplify individual agency but also concentrate control and harm democratic life. The authors identify four pivotal choices for executives and courts, Congress, states, and everyday users—centering on legal accountability, privacy and portability, reparative taxation, and consumer product choices. They urge proactive, aligned policy and civic action to avoid repeating past mistakes and to steer AI toward public-good outcomes.

🤖 The 2025 State of AI Data Security Report shows AI is widespread in business operations while oversight remains limited. Produced by Cybersecurity Insiders with Cyera Research Labs, the survey of 921 security and IT professionals finds 83% use AI daily yet only 13% have strong visibility into how systems handle sensitive data. The report warns AI often behaves as an ungoverned non‑human identity, with frequent over‑access and limited controls for prompts and outputs.