All news with #ai risk management tag

⚠️ Agentic AI is shifting from assistance to autonomous action, creating new risk vectors that can exponentially multiply the impact of errors or breaches. Organizations must adopt governance by design—defining approved use cases, data access, mandatory controls, and clear accountability—so agents operate within known limits. IT teams should lead deployment, policy, and third‑party oversight, while investing in targeted training and resilience planning to protect both systems and staff.

⚠️ Agentic AI systems introduce acute governance and security challenges because autonomous agents can plan, execute tools, and process sensitive data without human oversight. The OWASP Foundation's Top 10 catalog identifies threats such as goal hijack, tool misuse, privilege abuse, supply chain compromise, RCE, memory poisoning, insecure inter-agent communication, cascading failures, human-trust exploitation, and rogue agents, each with examples and mitigations. Kaspersky condenses those findings and emphasizes a layered, near-Zero Trust defense: least autonomy and privilege, short-lived credentials, human-in-the-loop for critical actions, execution isolation, intent gates, continuous logging, behavioral monitoring, supply chain controls, and targeted training.

🔒 Cybercriminals now operate like businesses—highly specialized, service-oriented, and ROI-driven—using models such as RaaS and initial access brokers to scale attacks. This industrialization, amplified by AI and automation, forces a shift from reactive detection to proactive prevention and identity-first controls. CISOs must prioritize governance, supply-chain resilience, defensive automation, and strategic partnerships to manage risk amid talent and budget shortfalls.

🛡️ Ransomware groups are increasingly threatening victims with regulatory complaints in addition to data leaks, citing alleged violations of rules such as GDPR. Security vendors including Akamai report the tactic has grown over the past two years and is used by gangs like Anubis and Ransomhub to pressure high-compliance sectors such as healthcare. Experts warn AI accelerates the process by quickly identifying 'material' issues and producing legally framed complaints, tightening deadlines and raising stakes for victims.

🖼️ Fraudsters are using AI and large language models to create highly convincing fake invoices, appraisal certificates and certificates of authenticity for artworks, making forgeries harder to detect. Brokers and appraisers, including Marsh, report that chatbots can invent plausible experts and documentation or hallucinate false references that owners accept as real. Insurers and valuation firms are now deploying AI-based metadata analysis and anomaly detection to flag manipulated provenance and guide human review.

🔐 This article examines the security and governance challenges of generative AI and outlines practical steps organizations can take to reduce risk. It highlights model limitations such as hallucinations and underscores the continued need for human oversight for high‑stakes decisions. The author reviews prominent standards including NIST AI RMF, AICM and CSA Model Risk Management, and stresses cloud shared‑responsibility, cross‑team governance, and targeted workforce training as core mitigations.

🔍 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.

🛡️ 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.

⚠️ This guide outlines the principal risks generative AI (GenAI) poses to organizations, categorizing concerns into internal projects, third‑party solutions and malicious external use. It urges inventories of AI use, application of risk and deployment frameworks (including ISO, NIST and emerging EU standards), and continuous vendor due diligence. Practical steps include governance, scoring, staff training, basic cyber hygiene and incident readiness to protect data and trust.

🔔 AWS has published one new Responsible AI lens and updated Generative AI and Machine Learning lenses to guide safe, secure, and production-ready AI workloads. The guidance addresses fairness, reliability, and operational readiness while helping teams move from experimentation to production. Updates include recommendations for Amazon SageMaker HyperPod, Agentic AI, and integrations with Amazon SageMaker Unified Studio, Amazon Q, and Amazon Bedrock. The lenses are aimed at business leaders, ML engineers, data scientists, and risk and compliance professionals.

🔒 The energy sector faces a high and growing cyber threat, with attackers targeting OT systems, grid sensors and IoT endpoints to create cascading societal impacts. Critical vulnerabilities — notably in Siemens products — and increasing IT‑OT coupling widen the attack surface. The article stresses the need for end-to-end visibility, AI-driven early warning and anomaly detection, and stronger international cooperation, including NIS 2-aligned practices and active CERT coordination to build resilience.

🚨 Google Cloud Security's Cybersecurity Forecast 2026 warns of a rise in cyber-physical attacks across EMEA targeting energy grids, transport and digital infrastructure. The report highlights increased state-sponsored espionage from Russia and China and anticipates these operations may form hybrid warfare combined with information operations to erode public trust. It also flags supply-chain compromises of managed service providers and software dependencies, and notes that cybercrime — including ransomware aimed at ERP systems — will remain a major disruptive threat to ICS/OT. Analysts further expect adversaries to increasingly leverage AI and multimodal deepfakes.

🛡️ AI is a strategic imperative for modernizing risk management, enabling organizations to shift from reactive to proactive, data-driven strategies. Manfra highlights four practical AI uses—risk identification, risk assessment, risk mitigation, and monitoring and reporting—and shows how NLP, predictive analytics, automation, and continuous monitoring can improve coverage and timeliness. She also outlines operational hurdles including legacy infrastructure, fragmented tooling, specialized talent shortages, and third-party risks, and calls for leadership-backed governance aligned to SAIF, NIST AI RMF, and ISO 42001.

🤖 Agentic AI burst into prominence with promises of streamlining operations and accelerating productivity. This Special Report assesses what's practical versus hype, examining the current state of agentic AI, the primary deployment challenges organizations face, and practical lessons from real-world success stories. It highlights business processes suited to agentic agents, criteria for evaluating development tools, and how LinkedIn built a platform. The report also outlines near-term expectations and adoption risks.

🔍 Join a free expert webinar that translates rapid AI advances into practical, actionable tactics for Governance, Risk, and Compliance (GRC) teams. The session will showcase real-world examples of AI improving compliance workflows, early lessons from agentic AI deployments, and the common risks teams often overlook. Expect clear guidance on mitigation strategies, regulatory gaps, and how to prepare your team to make AI a competitive compliance advantage.

🔍The Brookings report details the Trump administration’s expanded social media surveillance to identify and punish foreign nationals for public speech. Agencies historically gathered millions of handles, but Secretary of State Marco Rubio has promoted a zero-tolerance “Catch and Revoke” policy that uses AI to flag conduct deemed contrary to national interest. Rubio said about 300 visas—mainly student and visitor visas—were revoked, and a State Department cable now requires student applicants to set accounts public for vetting.

⚠️ EY polling of 100 UK firms finds that nearly all respondents (98%) experienced financial losses from AI-related risks over the past year, with an average loss of $3.9m per company. The most common issues were regulatory non-compliance, inaccurate or poor-quality training data and high energy usage affecting sustainability goals. The report highlights governance shortfalls — only 17% of C-suite leaders could identify appropriate controls — and warns about the risks posed by unregulated “citizen developer” AI activity. EY recommends adopting comprehensive responsible AI governance, targeted C-suite training and formal policies for agentic AI.

🔍 Paul Dongha, NatWest's head of responsible AI and former data and AI ethics lead at Lloyds, highlights the ethical red flags CISOs and boards must monitor when deploying AI. He calls out threats to human agency, technical robustness, data privacy, transparency, bias and the need for clear accountability. Dongha recommends mandatory ethics boards with diverse senior representation and a chief responsible AI officer to oversee end-to-end risk management. He also urges integrating audit and regulatory engagement into governance.

🔍 The essay examines how AI is reshaping U.S. politics heading into the 2026 midterms, with campaign professionals, organizers, and ordinary citizens adopting automated tools to write messaging, target voters, run deliberative platforms, and mobilize supporters. Campaign vendors from Quiller to BattlegroundAI are streamlining fundraising, ad creation, and research, while civic groups and unions experiment with AI for outreach and internal organizing. Absent meaningful regulation, these capabilities scale rapidly and raise risks ranging from decontextualized persuasion and registration interference to state surveillance and selective suppression of political speech.

🔐 October marks Cybersecurity Awareness Month, underscoring that the human element is the first and most critical line of defense against cyberthreats. Cybercriminals exploit social engineering and increasingly rely on AI-driven tools to create believable, hyper-personalized scams and deepfakes. Watch the video with ESET Chief Security Evangelist Tony Anscombe for practical insights, and consider ESET's cybersecurity awareness training to strengthen individual and organizational resilience.