Agentic AI – autonomous, goal-driven artificial intelligence – is rapidly transforming both business operations and the threat landscape. Defined by its independence, proactivity, and adaptability, agentic AI promises efficiency gains in sectors like finance and healthcare.

However, this same autonomy makes it a silent saboteur in cybersecurity: an emerging class of threats operating with minimal human oversight. In fact, artificial intelligence has now eclipsed ransomware as the top concern for security leaders, with 29% citing AI and LLMs as their number one risk in 2025 (versus 21% for ransomware).

In highly regulated industries, the stakes couldn’t be higher – sensitive data, compliance obligations, and stakeholder trust hang in the balance as agentic AI redefines how cyber-attacks are conceived and executed.

New Autonomous Threats in Regulated Sectors

Traditional cyber threats are being supercharged by AI acting as an intelligent agent. Cybercriminals are leveraging agentic AI to automate complex, multi-stage attacks that unfold much faster than any human-led operation. For example, researchers at Palo Alto Networks’ Unit 42 showed that AI-orchestrated ransomware could go from initial breach to data exfiltration in just 25 minutes – a speed nearly impossible to counter with manual incident response. These AI “agents” can adapt their strategies on the fly, employing polymorphic malware that rewrites itself to evade detection, or operating filelessly in memory to stay invisible to traditional defences.

Equally troubling is the rise of “shadow AI.” Employees in finance and healthcare are increasingly using unsanctioned AI tools at work – in one survey, 78% admitted to doing so. These unvetted tools (e.g. an analyst using a third-party AI to model risk) create blind spots and compliance risks, as sensitive data may be processed without proper oversight. Agentic AI’s autonomy also means it can make significant decisions without human review. Imagine an AI-driven trading algorithm initiating large transfers or an AI triage system making patient care decisions; if these agents err or are manipulated, the consequences range from financial loss to endangering lives. Indeed, incidents of unintended AI actions, like erroneous trades or misdiagnoses are rising as autonomy grows.

Regulated industries face unique vulnerabilities with these “silent” AI threats. Multi-agent systems might inadvertently share or leak confidential data during collaboration; in tests, 31% of AI agents unintentionally exposed sensitive PII or health data to other agents. The complex web of third-party APIs that modern AI solutions rely on also introduces new entry points. A single insecure API integration can lead to breach: a misconfigured AI service at a healthcare vendor recently exposed 483,000 patient records. Meanwhile, threat actors are also using agentic AI offensively – automating reconnaissance, spear-phishing, and exploit development. The result is an onslaught of autonomously orchestrated attacks that can strike with speed and sophistication that challenge even well-prepared security teams.

Governing AI Risks – Frameworks and Controls Emerge

As agentic AI proliferates, organisations must evolve their risk management and governance. Industry standards are beginning to address these challenges. The new NIST AI Risk Management Framework offers guidance for governing AI risks via mapping, measuring, and controlling practices. Likewise, ISO/IEC 42001 (2023) introduces AI lifecycle management systems with built-in risk controls and accountability mechanisms. Regulatory bodies are also reacting – for instance, the UK’s Financial Conduct Authority is testing AI regulatory sandboxes to safely explore oversight techniques.

A common theme is emerging: visibility, accountability, and “explainability” must be engineered into AI systems from the start. Organisations are adopting explainable AI tools (e.g. SHAP, LIME) to interpret agent decisions and create an AI Bill of Materials (AI-BOM) to inventory models, data, and dependencies for auditing.

Effective governance also means enforcing constraints on autonomous AI. Human-in-the-loop checkpoints for high-impact decisions, automated audit trails of AI actions, and strict data handling policies are becoming non-negotiable in regulated sectors.

Change management protocols now require that any agentic AI deployment undergo rigorous review for compliance, bias, and security. For example, financial firms are instituting restricted AI vendor lists and annual algorithm audits to ensure AI tools meet security and regulatory standards. In healthcare, AI diagnostic agents are being paired with mandatory physician review for critical outcomes.

These measures align with broader cybersecurity best practices – such as zero-trust architectures and least-privilege access now being extended to AI systems. Notably, AI-specific incident response playbooks are emerging as well, detailing how to rapidly disable or override rogue AI behaviours in a crisis.

A DEEP Approach to Resilience and Readiness

Confronting agentic AI threats requires a proactive and structured approach. NuroShift applies the DEEP framework (Define, Evaluate, Execute, Progress) to help organisations stay ahead of these risks.

In the Define phase, we work with stakeholders to identify where AI is deployed (or shadow-deployed) in critical workflows and map out potential failure points or threat scenarios. For example, mapping an AI-driven loan approval system might reveal risks of unauthorised model changes or data poisoning.

During Evaluate, we assess the severity and likelihood of those scenarios – leveraging frameworks like NIST AI RMF – and gauge current controls. This can include stress-testing AI models through red-team exercises and adversarial simulations to see how they handle manipulation or adaptive attacks. Gaps are prioritised by impact and feasibility, ensuring attention to high-risk issues like unchecked fund transfers or PHI exposure.

In the Execute phase, NuroShift guides the implementation of safeguards. This might involve deploying monitoring tools for AI decisions, integrating AI-powered Security Automation to continuously watch for anomalies, or establishing an AI governance committee to approve new use cases. Often, a key execution step is improving AI Risk & Governance policies – for instance, formalising guidelines on employee use of AI (to curb shadow AI) and requiring security evaluations for any AI vendor or API integration. We also help clients implement technical controls like encryption and robust identity verification around AI systems, drawing on services in AI Security Automation to embed security into AI workflows. Crucially, staff training accompanies these changes: through AI Cybersecurity Training workshops, we up-skill security teams and business units on AI threat awareness, safe AI development practices, and incident response procedures.

Finally, Progress is about continuous improvement. Agentic AI threats will evolve, so we establish ongoing review cycles. This includes periodic readiness assessments (using our AI & Cybersecurity Readiness Assessment model) to reassess maturity across domains like architecture, data governance, and workforce readiness. Metrics from these assessments feed into an actionable AI Strategy & Roadmap, aligning future investments (e.g. in AI model validation tools or advanced monitoring) with identified gaps.

From Saboteurs to Strengths – Turning Risk into Readiness

Agentic AI may be a silent saboteur today, but with the right strategy it can become tomorrow’s asset. The very traits that make these AI systems risky – autonomy, speed, adaptability – can equally be harnessed for defence. The question for executives in regulated industries is how to assert control over the uncontrolled. Will AI work for us or against us? The answer lies in preparedness. By treating AI not as a black-box wizard but as another domain of enterprise risk to be governed, organisations can neutralise threats before they materialise. The latest data shows that awareness is rising, yet true preparedness remains dangerously low. In this inflection point, thoughtful leadership is paramount.

NuroShift invites you to reflect on your agency over AI: Are your “smart” systems aligned with your security and compliance values, or are they potential saboteurs in waiting? With a rigorous DEEP methodology and specialised services, we help turn fears of autonomous threats into confidence in autonomous defences.

In an era where AI will handle an ever-growing share of decisions, ensuring those decisions and indecisions don’t jeopardise your enterprise is the new mandate. The silent saboteurs are out there. Let’s define our response, evaluate our readiness, execute our plan, and progress toward a future where AI augments security rather than undermines it. The opportunity to act is now. By partnering with NuroShift, regulated organisations can secure the promise of agentic AI while disarming its perils.

Dive deeper into NuroShift’s AI Risk & Governance Advisory or schedule an AI Security Testing consultation to start fortifying your defences: https://www.nuroshift.ai/

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