Building Trust Through Clarity, Purpose, and Governance

As artificial intelligence becomes embedded in critical infrastructure (CNI)* — from energy and water to transport and justice — the demand for clearly defined purpose and robust governance grows sharper. With autonomy, inference, and adaptiveness now central to AI functions, the risks are no longer theoretical. They’re operational, societal, and reputational.

In this article, we explore the real challenges facing AI integration in secure environments and offer practical guidance for boards, CISOs, and operational leaders responsible for deploying AI systems responsibly.

Why Clarity Creates Trust in AI

The first step in governing AI effectively is defining what it is. Without clarity, trust collapses.

Most AI systems today are described by the innovators and vendors who build them. This results in opacity, inconsistency, and confusion for buyers — especially when these systems are embedded in functions that could operate autonomously and without oversight.

To solve this, the OECD developed the first intergovernmental definition of AI, now adopted into:

The EU AI Act (Regulation 2024/1689)
International standard ISO/IEC 22989
The Council of Europe Framework Convention on AI, Human Rights, Democracy and the Rule of Law.
And guidance frameworks like the NIST AI RMF

This unified definition is there to enable a consistent dialogue across procurement, legal, technical, and operational teams. It supports legal certainty, informed decision-making, and improved risk posture.

‘AI system’ means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments

AI in CNI is High-Risk – and High-Opportunity

Approximately 10–15% of all AI systems are considered high-risk under the EU AI Act. These include systems for surveillance, biometric identification, access control, predictive analytics, and autonomous decision-making. Furthermore, CNI AI systems fall squarely within this category.

Key legal obligations for high-risk systems include:

Transparency, cybersecurity, and explainability
Conformity assessments and technical documentation
Pre- and post-market monitoring
Human oversight and documented escalation protocols

These mechanisms help control AI drift, avoid over-dependence on automation, and protect against prohibited use cases — such as systems that cause significant harm through manipulative techniques.

The Arup deepfake incident, where £25 million was transferred based on a fake video and call, shows what can happen when AI risk is unexpected.

Narrow AI vs General Purpose AI: What Are You Dealing With?

Most CNI applications today use narrow AI — purpose-built models trained on specific types of data for defined tasks:

Facial recognition
Person detection
Object classification
Risk prediction

These systems often operate with a degree of autonomy (e.g. access control), but must be validated for inclusivity, error rates, and real-world impact.

On the other hand, General Purpose AI (GPAI) — including foundation models, LLMs and generative AI — is adaptable, multimodal, and capable of cross-functional insight generation. When applied to unstructured operational data, it enables:

Acceleration of knowledge-based tasks
Pattern correlation across disparate data sources
RAG systems that use internal knowledge, not public training data

GPAI offers transformation. But without skilled oversight, it also amplifies risks.

Agentic AI: The Next Step – and the Next Challenge

Agentic AI refers to systems capable of reasoning over dynamic conditions, adapting autonomously, and making decisions with minimal human intervention.

In CNI, this could mean:

Real-time autonomous decision-making
Cyber threat detection and automatic response
Predictive maintenance across sensor networks
Resource optimisation (energy, water, logistics)

These applications require simulation environments, high-integrity sensor data, and human-in-the-loop safeguards to be safely deployed.

Key Challenges to Address Now

CNI sectors face seven core challenges in AI adoption:

Accuracy vs. Trust – especially for complex value chain and life-impacting systems
Data Quality – what data is available and is it to a quality? e.g., FAIR data standards (e.g. ISO/IEC 5259)
Data Privacy & Personally Identifiable Information – should the data be used at all? is the use case compliant with GDPR?
Inference Risk – what patterns might your AI surface, e.g., identification, profiling, targeting, exclusion.
Proof of Concept Failure – over 70% never reach production. Purpose and governance can bridge the gap between experimentation and operational success
Cybersecurity – do you know what questions to ask your cloud provider?
AI Literacy Gaps – all users including decision-makers must understand the system

Opportunities in CNI

While many AI applications are still in pilot or proof-of-concept stages, CNI organisations are already seeing results:

Sector	Use Case
Energy & Utilities	AI-driven anomaly detection, real-time monitoring
Transport	Predictive maintenance, real-time threat detection
Water & Waste	AI-enhanced cyber-physical security
Prisons	Behaviour inference, facial recognition, access control

Governance is the Enabler

Some regions have adopted a light-touch regulatory stance on AI. However, for CNI, a strong governance posture is essential — and that’s where ISO/IEC 42001 comes in.

This new certifiable international standard provides:

End-to-end lifecycle controls
Evidence-based trust mechanisms
Risk frameworks aligned with business outcomes

At Anekanta®, we support organisations in adopting ISO/IEC 42001 as a “trust mark” for responsible AI. It’s the answer to the question: “How do we know this AI system can be trusted in our value chain?”

Takeaway Actions for CNI Leaders

In CNI, leadership is the ultimate safeguard. The time for passive observation is over. Responsible AI governance must now be a boardroom priority. We have provided free guidance to get you started on your AI journey.

Develop a formal AI strategy and governance policy.
Create clear business cases for AI investments.
Build board-level AI literacy and human oversight.
Adopt a cybersecurity-first approach to AI procurement.
Consider on-premise AI for secure environments.
Prepare for EU AI Act compliance and formal management system certification (ISO/IEC 42001).

*In this article, we use the terms Critical National Infrastructure (CNI) and critical infrastructure interchangeably to reflect real-world sector practice.

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Key EU AI Act Deadlines
The EU AI Act – Regulation (EU) 2024/1689 – effective from 1 August 2024 and in application 2 August 2026.
Chapter I and II including AI Literacy and Prohibitions are in application 2 February 2025.
Chapter V GPAI requirements are in application 2 August 2025 (enforced 2 August 2026)
Find out about our EU AI Act pre-compliance services
Explore our EU AI Act Resource Centre