McKinsey: Only 1% of companies have mature AI adoption. How to improve.

Agentic AI offers immense potential but introduces a new class of risks. McKinsey identifies five emerging threats: chained vulnerabilities, cross-agent task escalation, synthetic-identity risk, untraceable data leakage, and data corruption propagation. CIOs, CISOs, and CROs must treat AI agents as “digital insiders,” strengthening governance, IAM, and oversight. Establishing traceability, contingency planning, and secure agent-to-agent protocols is essential. In an agentic world, trust cannot be assumed—it must be architected into every layer of AI deployment. #AgenticAIRisks #AIsecurity #ResponsibleAI https://lnkd.in/gs6ewfDP

A whole new consulting industry is emerging around agentic AI risk analysis and abatement. The gap is massive: only 1% of organizations believe their AI adoption has reached maturity, yet 80% have already encountered risky AI agent behaviors. McKinsey’s new playbook highlights novel risks we haven’t seen before—chained vulnerabilities, synthetic-identity risks, and untraceable data leakage across agent networks. These aren’t traditional cybersecurity problems. Trust can’t be a feature of agentic systems; it must be the foundation. How are you approaching agentic AI governance? https://lnkd.in/gDepNmv7

AI agents introduce novel risks. Leaders must embed safety & security across the entire agent lifecycle—from design to deployment—using a proactive framework to manage threats & ensure trust.

A McKinsey & Company report highlights a profound shift: #AI is moving from enabling interactions to driving transactions that directly run your business. This leap in autonomy creates new, amplified #risks. McKinsey identifies 5 critical new threats: ⛓️ Chained Vulnerabilities: One agent's error (like a bad data point) cascades, leading to a majorly flawed outcome (like a risky loan approval). ⬆️ Cross-Agent Task Escalation: Malicious agents exploit "trust" between systems, impersonating privileged users (like a doctor) to steal sensitive data. 🎭 Synthetic-Identity Risk: Forget fake humans. Attackers can now forge the digital identity of a trusted agent to bypass security. 💧 Untraceable Data Leakage: Agents interacting autonomously can share sensitive PII without leaving an audit trail. The leak is invisible. 🗑️ Data Corruption Propagation: A single piece of low-quality data (e.g., a mislabeled trial result) is silently fed to multiple agents, poisoning the entire decision pipeline. The Takeaway 🚨 These errors erode all faith in automated processes. The efficiency gains from agentic AI are worthless if built on a foundation of sand. Safety and security can't be an afterthought. They must be woven in from day one. 🦺 #AgenticAI #McKinsey #AIStrategy #AIRisk #DigitalTrust #Cybersecurity #AIgovernance #Innovation https://lnkd.in/dQuPkUUJ

As organizations embrace agentic AI, the potential for transformative efficiency is immense, yet so are the risks. The shift towards autonomous systems necessitates rigorous governance and proactive risk management to prevent vulnerabilities that could disrupt operations or compromise data integrity. Establishing robust oversight and security frameworks is crucial to ensure that these intelligent agents operate within ethical and secure boundaries, fostering trust while maximizing value. The future of AI demands not just innovation but a commitment to safety as foundational. #cybersecurity #riskmanagement #agenticai

AI just ran its first real cyberattack. Anthropic caught it mid-stride. Attackers manipulated Claude Code into infiltrating ~30 organizations finance, chemicals, even government agencies with 80–90% of the op executed autonomously. How? By breaking malicious tasks into “innocent” micro-requests to slip past guardrails. Analysts call it state-sponsored. Confidence: high. Here’s what just changed: Speed & scale: Agentic attacks iterate faster than your SOC can triage. Deception-by-design: Harmless subtasks = plausible deniability for both model and human. Supply-chain risk: Your AI vendors just became part of your attack surface. What to do about it Agent kill-switches + action budgets: Cap rate, steps, and timeouts. Add human hold-points. Least-privilege tool use: Scoped creds. Short-lived tokens. Vault everything. Behavioral telemetry: You can’t log chain-of-thought—but you can log tool traces. Pre-prod gauntlet: Red-team with adversarial prompts, task-splits, goal-hijacks. Vendor clauses: Demand jailbreak reporting, continuous evals, and incident SLAs. Exec takeaway: AI won’t just write your runbooks—it’ll attack them. Treat every agent like a junior contractor with root access: log it, limit it, and be ready to kill it on demand. — CJ 🧠 | Integrate • Automate • Scale #AISecurity #AgenticAI #Cybersecurity #ModelGovernance #RiskManagement #Claude #Anthropic #CISO

Senior Executives part 4 🛑 Pain: The Catastrophic Cost of Unmanaged Risk The financial and reputational cost of a single major data breach or regulatory fine is no longer a budget line item—it's catastrophic, often starting at $10 million and climbing quickly. The real challenge? Human security teams simply cannot keep pace with the volume and sophistication of threats hitting the network 24/7. This gap is the executive's largest latent liability. The Executive Mandate: AI as Risk Insurance You can't afford reactive security in an AI-driven world. Investment in this area is not optional; it's the premium for business survival. A AI Consultant Architect who specializes in deploying AI-Powered Threat Detection & Compliance ecosystems is the answer. They use machine learning to analyze network activity in real-time, instantly detect subtle anomalies, and secure your systems before the breach hits. Stop relying on tired security models. It’s time to architect a defensible, proactive risk posture. TheMSeries.com #Cybersecurity #RiskManagement #AIfordefense #RegulatoryCompliance

Zero Trust Has a Blind Spot—Your AI Agents #wortharead First if you think of AI as an agent, and I can understand why, because we call it that, don't think of it as an agent. It is Agentic, meaning it is Agent like ... acting on behalf of a person or persons. Name those people. They are the responsible people for that tool, that agentic ai tool. Long before this issue we have had tools that "suddenly", that is without discussion or approval or even awareness, started accessing databases and process flows. Typically some other IT department or 3rd party contractor would come in and need to access the data. They knew, or sometimes did not when it was Shadow IT related, that there would be questions and requirements, and they were in a hurry or simply unaware of the risks. We typically found out when performance issues cropped up, or after an update caused problems for their tools. Anyway, you can solve many of these challenges with a strong data access and management governance policy... backed up with solid monitoring. In this case make sure a person owns this tool and make sure your data is already classified and managed in a manner that requires any tool, including an agentic ai system, to abide by the rules you have in place for your data. #datagovernance #digitalmanagement #toolmanagement #privacy #security https://lnkd.in/excCJP55

It is crucial to prioritize safety and security as you navigate the complexities of agentic AI. McKinsey's latest playbook outlines best practices for AI governance, cybersecurity risk assessment, and autonomous system management. https://okt.to/Hm2Bck

When AI Turns Rogue: Mitigating Insider Threats in the Age of Autonomous Agents -  The prolific rise of AI agents is creating new challenges for security and IT teams. On the cusp of this shift towards more agent-automated workflows for business continuity tasks, recent testing found that AI agents can exhibit unsafe or deceptive behaviors under certain conditions, creating a new insider threat for businesses across industries. Presenting a critical need for organizations to properly monitor AI agents that are accessing sensitive data and acting without human oversight, this potentially introduces new classes of risk that are faster, less predictable, and harder to attribute. The reality of these risks is twofold. On one hand, […] - https://lnkd.in/eayXsptV