Public sector organisations and critical infrastructure operators are facing a new challenge: how to adopt AI securely and responsibly in high-risk environments. From utilities and healthcare to transport and government services, AI is increasingly being used to support automation, analytics and decision-making. The focus for IT and CISO leaders attending the Elevate Tech Summit and Cyber Secure Forum is shifting beyond innovation towards governance, resilience and risk management…
The expanding threat landscape
AI systems introduce new attack surfaces and operational risks. Unlike traditional software, machine learning models rely heavily on data quality, training integrity and ongoing monitoring. For critical infrastructure operators, this creates concerns around:
- Manipulated or poisoned training data
- Adversarial attacks designed to mislead AI models
- Unauthorised access to sensitive datasets
- Lack of transparency in automated decision-making
In sectors where operational continuity and public trust are essential, these risks can have significant consequences.
Public sector pressures around governance
Public sector organisations face additional scrutiny around data privacy, ethics and accountability. AI systems must comply with UK data protection regulations while also aligning with broader expectations around fairness and transparency. This is particularly important where AI is used in:
- Citizen services and case management
- Resource allocation and prioritisation
- Security monitoring and threat detection
Leaders must be able to explain how AI-driven decisions are made, and demonstrate that systems are operating fairly and securely.
Building secure AI environments
To manage these risks, organisations are strengthening governance across the AI lifecycle. Key priorities include:
- Securing training and operational data environments
- Implementing strict identity and access controls
- Monitoring models continuously for drift or abnormal behaviour
- Maintaining audit trails for AI-generated decisions
Security teams are also increasingly involved earlier in AI development processes, ensuring governance is embedded from the outset rather than added retrospectively.
Managing third-party AI risk
Many organisations rely on external AI platforms and cloud-based services. This increases the importance of supplier due diligence and third-party risk management. IT and security leaders should assess:
- Where data is stored and processed
- How models are trained and updated
- Whether AI outputs are explainable and auditable
- Supplier compliance with UK regulatory and security standards
In critical infrastructure environments, resilience and operational continuity should be central considerations.
Balancing innovation with control
One of the biggest challenges for CISOs is enabling innovation without creating unacceptable risk. Overly restrictive governance can slow adoption, while weak controls may expose organisations to operational or reputational harm.
Leading organisations are addressing this through cross-functional AI governance frameworks, bringing together IT, security, legal, compliance and operational stakeholders.
Building trust in AI adoption
For public sector and critical infrastructure leaders, successful AI adoption depends not only on technical capability, but on trust, transparency and resilience.
By embedding strong governance, securing data and scrutinising supplier practices, organisations can harness the benefits of AI and machine learning , while ensuring systems remain secure, compliant and fit for critical environments.
Are you searching for AI solutions for your organisation? The Cyber Secure Forum can help!
