Securing Agentic AI: Resilience for nations, enterprises and society

Agentic AI, autonomous systems capable of independent action, is no longer a distant research concept. From the trading floor to enterprise operations, these AI agents are beginning to make decisions, execute tasks, and act without continuous human oversight.

Early deployments by organisations like J.P. Morgan, CrowdStrike, and OpenAI demonstrate the transformative potential of this technology. Yet as the capabilities of these systems grow, so too does the need for careful governance, security, and oversight, particularly in critical national infrastructure (CNI) and highly regulated environments.

The question is simple, but profound: what will it take for agentic AI to be safely adopted at scale? 

The societal stakes: the good, the bad, and the ugly

Agentic AI promises to boost productivity, improve public services, and enhance societal resilience. Imagine AI agents autonomously managing traffic flows in smart cities, optimising energy grids, or accelerating research in healthcare and pharmaceuticals. Enterprises like IQVIA, for example, are exploring fleets of AI agents to streamline operations and support data-driven decision-making at unprecedented scale.

However, the same technology carries significant risks. Misuse, over-reliance, or operational failures could magnify human error, spread disinformation, or disrupt essential services. Shadow agentic AI and unsanctioned systems deployed by employees further complicate the landscape. These agents, operating outside governance structures, can manipulate contracts, automate unethical shortcuts, or trigger cascading failures across multiple systems.

The imperative is clear: we must harness agentic AI’s potential while limiting its blast radius. This requires technical guardrails, cultural awareness, and robust governance frameworks.

Lessons from the frontline: Agentic AI in public healthcare

Public healthcare providers exploring agentic AI can unlock transformative benefits, including automating routine tasks, optimising operational workflows, and accelerating insights that improve patient outcomes. However, deployment comes with significant challenges around governance, explainability, and security:

1. Data sovereignty: Patient data is highly sensitive and cannot leave controlled environments. Healthcare organisations must implement locally hosted models or secure cloud platforms with strict data boundaries to maintain privacy and regulatory compliance.
2. Tame autonomy: AI agents require strict operational guardrails. Human-in-the-loop verification, kill switches, least-privilege access, and controlled toolsets are essential to prevent unintended actions in safety-critical environments.
3. Edge-case preparedness: Agents will inevitably encounter scenarios beyond their training. Real-time monitoring, incident response playbooks, and adversarial testing are critical to identifying vulnerabilities and mitigating risk.

These lessons highlight a broader principle: while agentic AI has the potential to enhance efficiency and care delivery, adoption must be deliberate, secure, and incremental. Establishing robust governance, operational safeguards, and ongoing oversight is essential for public healthcare providers to safely realise AI’s potential while protecting patients, sensitive data, and public trust.

Collaboration: building trust across sectors

No organisation can address agentic AI risks alone. Governments, innovators, and enterprises must collaborate to develop trust and assurance frameworks. Shared standards for auditing AI decisions, verifying model robustness, and certifying safe autonomy will accelerate adoption while reducing systemic risk. Emerging tools, such as those developed by Secure Agentics, provide real-time behavioural monitoring, simulation testing, and utility-based decision engines, enabling AI agents to act safely within defined parameters.

Regulation is evolving in parallel. The EU AI Act, NIS 2, and ISO 42001 increasingly mandate traceability, explainability, and proof of controls. Organisations that integrate these compliance artefacts into operational pipelines today will remain ahead of the curve tomorrow.

Securing the foundations: key technical priorities

1. Data privacy isnon-negotiable
   Agentic AI thrives on data, but sensitive information cannot be exposed to unnecessary risk. For critical environments, there are two primary strategies:

• Locally hosted models: Provide full data sovereignty but require significant compute resources and technical expertise.
• Secure cloud environments: Maintain enterprise data boundaries while leveraging frontier AI models, endorsed by NCSC for certain use cases.

Both approaches ensure that AI agents operate within trusted limits, reducing exposure without sacrificing capability.

2. Tame autonomy
   Autonomy must be constrained by design. Rule-based guardrails, utility-based decision engines, and human-in-the-loop verification prevent catastrophic decisions. Even when agents make mistakes, the damage should be containable. For example, avoiding hours of industrial downtime or compromised safety in critical systems.
3. Prepare for edge cases
   Unexpected behaviour is inevitable. Agents should feed detailed logs into enterprise security stacks, and incident response playbooks must map out high-impact scenarios. Red teams should conduct ongoing adversarial testing to uncover vulnerabilities before they are exploited.
4. Account for dependencies
   AI agents rely on complex software ecosystems, including APIs, plugins, and open-source libraries. These dependencies carry hidden risks. Maintaining an AI asset register, a comprehensive inventory of every model, plugin, and data source, is essential for supply chain security.

The positive vision: transformative potential

Despite the challenges, agentic AI offers a compelling vision:

• Economic growth: Enterprises can accelerate decision-making, optimise resources, and innovate faster.
• Enhanced public services: Smart cities, healthcare, and energy infrastructure benefit from autonomous optimisation.
• Fortified national security: AI agents can support defence, threat detection, and crisis response in ways humans alone cannot.

Achieving these outcomes requires a holistic approach that combines security, governance, regulation, and cultural readiness. Strong guardrails, rigorous testing, and cross-sector collaboration are non-negotiable.

Delivering on the promise

Agentic AI is a paradigm shift. These agents are not mere tools; they are autonomous decision-makers that can amplify productivity and innovation, but also systemic risk. Securing them requires borrowing lessons from zero-trust networking, safety-critical systems, and secure software engineering, applied simultaneously and without exception.

As with any transformative technology, the first step is understanding what “good” looks like. Clear governance frameworks, transparent decision-making, robust security controls, and proactive risk management form the foundation of responsible agentic AI adoption.

We are not fully there yet. But the path forward is visible. By acting deliberately, learning from early deployments, and collaborating across governments, innovators, and enterprises, we can unlock agentic AI’s full potential: safely, responsibly, and for the benefit of society at large.