Ayaz Rosén’s Post

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

As the AI boom has continued to escalate at an exponential rate, leaving cybersecurity concerns behind is a prescription for disaster. AI Agents and Agentic AI raise the stakes even higher. This McKinsey & Company report illustrates the dangers and the steps IT teams must take to keep the company jewels safe. Don't wake up to find yourself unawares like the Louvre Museum did today. https://lnkd.in/e7avhFnD

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

According to McKinsey & Company research, just 1% of surveyed organizations believe that their agentic AI adoption has reached maturity. The journey begins with updating risks and governance frameworks, moves to establish mechanisms for oversight and awareness, and concludes with implementing security controls. Techstra Solutions can help accelerate your journey. #AgenticAI #riskmanagement #governance #security #digitaltransformation https://smpl.is/adk7u

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

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 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

When Agents Go Rogue: Security in the Age of Agentic AI We’ve entered a new chapter of AI — one where systems don’t just assist us, they act for us. As highlighted by McKinsey & Company, agentic AI marks a shift from interaction to transaction. These autonomous agents can make decisions, execute actions, and influence outcomes in real time. But with that autonomy comes a new set of risks that traditional controls weren’t designed to manage. Here are four emerging risk types that security leaders should pay attention to: 1. Chained Vulnerabilities A small flaw in one agent can cascade across others, amplifying impact. Example: A credit data agent mislabels short-term debt as income, inflating scores and leading to poor loan approvals. Preventive Control: Apply cross-agent validation and strong audit trails to catch cascading errors early. 2. Cross-Agent Task Escalation Compromised agents can exploit trust to gain unauthorized access. Example: A scheduling agent in a healthcare system poses as a doctor to retrieve patient data from a clinical agent. Preventive Control: Enforce role-based authorization and agent identity verification. 3. Synthetic Identity Risk Attackers can forge agent identities to bypass trust mechanisms. Example: A spoofed claims-processing agent requests policyholder data using fake credentials. Preventive Control: Use cryptographic attestation and apply zero-trust principles across agent communications. 4. Untraceable Data Leakage Agents can share sensitive data without oversight or proper logging. Example: A customer support agent includes unnecessary personal data when sending a query to an external fraud model. Preventive Control: Enforce data minimization, redaction, and audit visibility across agent interactions. Agentic AI holds enormous potential, but it also challenges our security assumptions. Protecting systems is no longer enough — we now need to secure entire ecosystems of autonomous decision-making. #CyberSecurity #AgenticAI #AIsecurity #McKinsey #RiskManagement #CISO #AIethics #EmergingTech #DigitalTrust #DataPrivacy #genai #genaisecurity #genairisk

AI + “Think Like a Thief” Explained. Adversarial Modeling - AI is trained to mimic the tactics of malicious actors (Eg:- Phishing, Fraud, Intrusion attempts). Red Team AI - Just like human red teams simulate attacks, AI agents can continuously probe systems for weaknesses. Predictive Defense - By learning attacker patterns, AI can forecast likely next moves and block them proactively. Cybersecurity:- • AI generates synthetic phishing emails to test employee resilience. • Models simulate brute-force or credential-stuffing attacks to harden authentication systems. • Continuous penetration testing by AI agents ensures systems evolve faster than attackers. Fraud Detection:- • AI mimics fraudster strategies in insurance, banking or e-commerce. • Detects anomalies by comparing “normal” Vs “attacker-like” behavior. • Example:- AI models trained on fraudulent claims can flag suspicious patterns before payouts. Compliance & Governance:- • AI simulates how someone might bypass ISO or ESG policies. • Predicts weak points in reporting or supply chain audits. • Ensures organizations close loopholes before regulators or bad actors exploit them. Imagine AI acting as a digital thief-in-training:- • It tries to break into your systems, falsify reports or manipulate procurement. • Every attempt is logged, analyzed and used to strengthen defenses. • The result? Resilient systems that are always one step ahead of real attackers. Traditional compliance and security are reactive responding after violations occur. Its time to change the process. Having successfully implemented and followed the same process, I now aim to integrate it with AI to amplify and scale the outcomes. AI that “thinks like a thief” flips the model to proactive defense, embedding resilience and transparency into every workflow. #AISecurity #PredictiveGovernance #ThinkLikeAThief

Autonomous, goal-driven AI agents present a transformative opportunity—but they also reshape the risk landscape fundamentally. McKinsey reports that roughly 80 percent of organisations have already experienced risky behaviour from AI agents—such as unauthorised data access or privilege escalation. These systems act more like digital insiders than passive tools, meaning that governance, identity, traceability and oversight must be built from the ground up. For technology leaders (CIOs, CISOs, CROs), the message is timely: • Upgrade your risk taxonomy to include agent-specific vulnerabilities—chained faults, cross-agent task escalation, synthetic identity attacks, and data-leakage via autonomous workflows. • Embed governance and traceability from day-one—define agent credentials, approval workflows, human-in-the-loop checkpoints, and logging of agent-to-agent communications. • Design for failure—every agentic deployment should come with sandboxing, rollback plans and simulation of worst-case behaviours. In short: agents are not simply a next-step in automation—they change the game entirely. Leaders who treat them with the same rigour they apply to their human teams will avoid creating tomorrow’s tech debt today. #AgenticAI #ResponsibleAI #AIGovernance #TechRisk #CyberSecurity #EnterpriseAI #CIO #CISO https://lnkd.in/emckUBgw