All news with #ai compliance tag

🛡️ ISACA is introducing an AI governance certification, currently in beta applications, slated for early 2027 to complement its existing AI credentials. The move follows research showing only 32% of digital trust professionals believe their organizations adequately address AI risks such as privacy, bias and security. ISACA’s broader AI credential suite already includes AAIA, AAISM and AAIR, and uptake has been strongest in Europe, the US and Asia according to recent polls.

🛡️ New research from Ox Security warns that Model Context Protocol (MCP) servers are creating an enterprise governance gap as AI adoption grows. MCP standardizes connections between AI models and external tools or data, but in doing so can bypass residency controls, zero trust boundaries and granular IAM policies. Analysis of public registries found many hosts outside the US and some unregistered, while permission models like “always-allow” enabled unauthorized data access in tested scenarios.

🛡️ The Solicitors Regulation Authority (SRA) has issued a warning to solicitors and law firms about using AI responsibly after spotting hallucinations and data leaks. The notice emphasizes that regulated individuals remain accountable for AI outputs and must maintain appropriate human oversight, governance and secure handling of client data. The SRA highlighted risks including false case citations, potential contempt of court and breaches of client confidentiality when information is entered into public AI tools.

🛡️ A coalition of 120+ tech organizations, led by members of NVIDIA’s Open Secure AI Alliance and coordinated via the Linux Foundation, unveiled the Shared AI Findings Exchange (SAFE) on August 4 to enable confidential information sharing on AI security incidents. The initiative emphasizes shared learning over blame and proposes confidential reporting, timely notification, collaborative analysis across the full AI stack, and independent governance to produce actionable, evidence-based defensive guidance. A public RFP invites broader community input, and proponents say SAFE can surface near misses and behavioral failures that traditional vulnerability disclosure processes miss.

🔍 Action1 surveyed over 1,000 sysadmins worldwide to compare 2024 expectations of AI and automation against the state of adoption in 2026. The report finds substantial shortfalls in areas such as patch management, monitoring, vulnerability prioritization and incident remediation, with predicted full automation far exceeding current implementation. Adoption is, however, growing selectively: many admins use AI for analysis and recommendations under supervised models while retaining authority over critical decisions. Concerns remain around privacy, accuracy, cost and job impact.

🔒 Organizations must ensure governance stays consistent when AI models or providers change. Portable governance anchors controls to the use case—covering identity, permitted purpose, data boundaries, output/action limits, and evidence—so policy follows the activity across models. An AI Gateway or control plane helps observe and enforce requirements across tools, teams, and deployments.

🔒 Over 70 cybersecurity organisations have signed the new CREST AI Charter, launched on July 9, committing to nine principles governing AI-enabled cybersecurity activities. The charter covers accountability and governance, transparency of use, documentation and auditability, boundaries and control, data handling and sovereignty, security and confidentiality, secure development, supply chain assurance and resilience. Signatories will maintain human oversight, document AI use, disclose data practices and implement secure development and supply chain controls. CREST intends the charter as a self-regulatory foundation to drive standards and harmonisation across industry and regulators.

🔒 This piece argues that the AI era is fundamentally different from prior technology waves and that organisations must adopt holistic, enterprise-wide governance rather than treating AI as solely a cybersecurity issue. The author emphasizes operational integrity, transparency, accountability, and the need for guardrail-style governance to enable safe innovation. It urges leaders to start building practical governance frameworks now and to involve CEOs, boards, and business units alongside security teams.

📝 A proposed AI Incident Reporting Act would obligate developers of designated high-capability models to report major safety and security incidents to the Commerce Department. Reports would be required within seven days of discovery, with 48-hour notifications to congressional leaders for imminent or ongoing serious harm. The bill tasks the Secretary of Commerce with defining capability thresholds and grants the department investigative and enforcement powers, including fines up to $2 million per violation.

📝 The authors critique Senator Bernie Sanders’s proposal for a US sovereign wealth fund that would take large equity stakes in AI firms. They agree on the need for public influence and redistribution of AI-generated wealth but warn public ownership can entangle government incentives with corporate profit. Instead, they recommend taxation (e.g., datacenter or AI token taxes) and a public AI option like Switzerland’s Apertus to promote transparency, sustainability and democratic control.

🔒 The White House Executive Order on advanced AI seeks practical public–private coordination to address AI-driven cyber risks while preserving innovation. It prioritizes voluntary model assessments, improved federal defenses, faster vulnerability discovery and remediation, and expanded cybersecurity talent. Successful implementation will hinge on operationalizing AI-assisted defense, translating insights into timely guidance and mitigations, and supporting resource-constrained critical infrastructure operators.

🛡️ The Trump administration has issued an executive order establishing a voluntary framework for developers of powerful AI models to submit a "covered frontier model" to US agencies for up to 30 days of cybersecurity review before wider release. The order explicitly forbids mandatory licensing or preclearance, tasks NSA, CISA and NIST with creating a classified benchmark to define covered models, and directs agencies to harden federal systems and expand AI-enabled defensive tools for smaller operators. It also creates an AI cybersecurity clearinghouse under the Treasury and leaves effectiveness dependent on possible future congressional action.

📰 Over eight years GDPR set global data-protection norms, notably the 72-hour breach notification standard, but nearly 40% of announced EU fines by value are annulled or under appeal. Experts say large tech firms contesting fines isn’t surprising and that rulings provide practical guidance for compliance teams. As the EU’s AI Act and proposed GDPR reforms arrive, regulators must shore up procedural robustness while organisations adapt governance to evolving AI risks.

🛡️ New RUSI research warns that adversaries, notably North Korea and Iran, are moving from AI-assisted to AI-enabled sanctions evasion and proliferation financing. The report highlights AI’s ability to mass-produce fraudulent documents, automate shell-company administration, and analyze blockchain flows to evade detection. Experts urge enterprises to adopt behavior-based analytics, defensive AI, stronger identity verification and updated training to counter these evolving threats.

🔒 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.

🛡️ New ISACA research finds AI tools are widely used in organizations, but governance is lagging. Ninety percent of digital trust professionals say employees use AI, yet only 38% report a formal, comprehensive AI policy while 25% have none at all. The poll highlights rising Shadow AI risks, with 56% unsure how long it would take to halt an AI system and only 20% having shutdown procedures, increasing exposure to data breaches and privacy failures.

🔍 Demos often hide the operational friction that causes many AI initiatives to stall once they move into production. What succeeds in a controlled presentation—clean data, crafted prompts, and fast isolated responses—rarely maps to fragmented security and IT environments with noisy inputs, latency constraints, and numerous edge cases. Teams that validate tools against real workflows, measure accuracy and latency under load, prioritize deep integration, clarify cost models, and embed governance early are far more likely to turn a promising demo into sustained production value.

🔒 The 2026 AI Cybersecurity Summit addresses the widening gap between rapid AI adoption and lagging security by focusing on practical, deployment-stage risk management. Speakers and sessions will explore visibility, governance, and layered protections across GenAI tools, custom models, APIs, and agentic systems. Attendees will receive operational guidance to secure AI as it moves from experimentation to production. The summit emphasizes integrating security, infrastructure, and operations to reduce accumulating risk.

🔐 EC‑Council launched its Enterprise AI Credential Suite, introducing four role-aligned certifications—Artificial Intelligence Essentials (AIE), Certified AI Program Manager (CAIPM), Certified Offensive AI Security Professional (COASP), and Certified Responsible AI Governance & Ethics (CRAGE)—alongside an updated Certified CISO v4. The suite is structured around the proprietary Adopt, Defend, Govern (ADG) framework to build practical capability across AI adoption, security, and governance. EC‑Council positions the expansion as a response to growing AI risk exposure and a pronounced workforce reskilling gap.

🔒 The Buyer’s Guide for AI Usage Control warns that AI adoption has far outpaced visibility and governance, producing a widening gap as AI is embedded across SaaS, browsers, copilots, extensions and shadow tools. It reframes the problem as an interaction issue rather than solely a data or app problem, and positions AI Usage Control (AUC) as a distinct governance layer that must discover and enforce policy at the moment of interaction. The guide outlines four operational stages—Discovery, Interaction Awareness, Identity & Context, and Real-Time Control—and stresses that architectural fit, operational overhead, and user experience are decisive factors when selecting a solution.