Protect Manufacturing Data from AI Ingestion in Alignment with the New Code of Practice: A Guide for UK Manufacturers

The UK manufacturing sector faces unprecedented challenges in protecting sensitive manufacturing data as AI transforms production environments. As Industry 4.0 initiatives accelerate and smart manufacturing becomes the norm, manufacturers must balance AI innovation with robust data protection. The UK Government’s new Code of Practice for AI cybersecurity provides essential guidance for manufacturers navigating this complex landscape.

Recent data from the Manufacturing Technology Centre reveals the scale of this challenge: 76% of UK manufacturers now employ AI systems across their operations, from predictive maintenance to quality control, with adoption expected to reach 90% by 2026. This widespread integration brings unprecedented opportunities for improving manufacturing efficiency but also introduces new risks to intellectual property and operational technology. The government’s new Code of Practice establishes crucial requirements for protecting these AI systems and the sensitive manufacturing data they process.

AI Risks in Manufacturing

The integration of AI in manufacturing environments presents unique challenges that demand specific attention under the new Code of Practice. Manufacturers must understand these risks to implement effective protection measures while maintaining operational efficiency and production quality.

Industrial Control Systems and Operational Technology

The protection of industrial control systems (ICS) represents one of the most critical areas requiring attention under the Code of Practice. Manufacturers must safeguard operational technology and production systems while enabling AI-driven optimisation. This delicate balance requires sophisticated security measures that protect against unauthorised AI access without compromising manufacturing operations.

James Wilson, Director of Digital Manufacturing at the High Value Manufacturing Catapult, emphasises this challenge: “Manufacturers must protect critical production systems while allowing AI to enhance operational efficiency. The Code of Practice provides crucial guidance for achieving this balance without compromising manufacturing capabilities.”

Production Data and Quality Systems

The protection of production data and quality management systems presents another critical challenge under the Code. As AI systems increasingly influence manufacturing processes and quality control, organisations must implement robust security measures that protect both the AI models and the sensitive production information they process.

Supply Chain Integration and Data Exchange

The integration of AI systems across supply chains introduces additional security considerations that the Code specifically addresses. Dr. Emma Roberts, Head of Industry 4.0 at a major automotive manufacturer, notes: “Modern manufacturing relies on complex data exchange throughout the supply chain. Protecting this information from unauthorised AI access while maintaining operational efficiency is crucial under the new Code.”

Organisations must implement sophisticated controls that protect:

• Supplier specifications and data
• Production schedules
• Inventory management information
• Quality control data
• Logistics planning

Aligning with the New Code of Practice

The Code mandates a sophisticated approach to risk assessment that goes beyond traditional industrial security evaluations. Manufacturers must now consider not only direct security risks but also potential vulnerabilities introduced by AI systems’ interaction with operational technology and production data.

Professor David Williams, Industrial Cybersecurity Lead at the University of Sheffield Advanced Manufacturing Research Centre (AMRC), explains: “Organisations must carefully evaluate how AI systems interact with industrial control systems and production data. The Code’s risk assessment requirements help manufacturers identify and address AI-specific vulnerabilities while maintaining operational continuity.”

Technical Implementation Requirements

The Code provides specific guidance for implementing security measures in manufacturing environments. Organisations must develop comprehensive security frameworks that protect sensitive manufacturing data while maintaining production efficiency. This includes:

Sophisticated access control systems that can manage AI system permissions while maintaining strict security standards. These systems must be capable of handling complex manufacturing workflows while preventing unauthorised access to critical production systems.

Advanced monitoring capabilities that can detect potential security incidents without impacting operational technology. Manufacturers must be able to track AI system behavior while maintaining the real-time responsiveness required for modern manufacturing operations.

Training and Awareness Requirements

The Code of Practice emphasises specialised training for manufacturing personnel, extending beyond traditional security awareness to focus specifically on AI-related risks and protective measures.

Operational Staff Development

Manufacturers must develop comprehensive training programs that address the unique challenges of protecting AI systems and manufacturing data. These programs should cover both technical security measures and operational considerations.

Mark Thompson, Director of Manufacturing Skills at Make UK, emphasises: “Production staff must understand both the potential and the risks of AI systems in manufacturing environments. This understanding is crucial for maintaining security while leveraging AI to improve operational efficiency.”

Integration with Safety Programs

Training programs must be integrated with existing safety and operational procedures, ensuring that security awareness becomes part of the organisational culture. This includes regular updates and refresher courses that address emerging threats and new protection requirements under the Code.

Incident Response and Recovery Planning

The Code mandates sophisticated incident response capabilities specifically designed for AI-related security events in manufacturing settings. Organisations must develop comprehensive plans that address both prevention and recovery while ensuring continuous production operations.

Response Framework Development

Manufacturers must establish clear procedures for identifying and responding to AI-related security incidents while maintaining critical operations. These procedures should include:

Immediate response protocols that can be activated without disrupting production. The response framework must balance security requirements with the need to maintain essential manufacturing processes.

Escalation procedures that ensure appropriate stakeholders are involved in incident management, including operational leadership and regulatory reporting when required.

Monitoring and Continuous Improvement

The Code emphasises ongoing monitoring and system enhancement. Manufacturers must implement sophisticated monitoring systems that provide real-time visibility into AI operations while supporting continuous security improvement and production quality.

Performance Metrics

Organisations should establish clear metrics for measuring the effectiveness of their security measures. These metrics should address both technical security requirements and operational impacts, providing a comprehensive view of security program effectiveness.

Adaptation and Enhancement

Security measures should be regularly reviewed and updated to address emerging threats and changing operational requirements. This includes:

• Regular assessment of security controls against evolving threat landscapes
• Updates to protection measures based on operational experience
• Integration of new security technologies as they become available

Next Steps for UK Manufacturing Firms

The UK’s new Code of Practice represents a crucial development in protecting manufacturing data from unauthorised AI access. Manufacturers must take decisive action to implement compliant security measures while maintaining efficient production operations and quality standards. Essential steps include:

Immediate Actions

Manufacturing organisations should begin by conducting thorough assessments of their current AI implementations and security measures. This evaluation should consider both technical requirements and impacts on production operations.

Strategic Planning

Organisations must develop comprehensive implementation strategies that address both immediate compliance requirements and long-term security objectives. These strategies should include clear timelines and resource allocation plans that account for manufacturing workflow requirements.

Ongoing Management

Successful implementation requires continuous monitoring and adjustment of security measures. Manufacturers should establish clear processes for ongoing management and improvement of their security programs while maintaining focus on production efficiency.

Implementing the Kiteworks AI Data Gateway

Manufacturing organisations can accelerate their compliance with the Code of Practice by leveraging Kiteworks AI Data Gateway. This comprehensive solution addresses key industrial sector requirements through:

Zero-Trust AI Data Access: The platform implements rigorous zero-trust principles specifically designed for AI interactions with manufacturing data and operational technology. This aligns directly with the Code’s requirements for strict access controls and continuous verification in manufacturing environments.

Compliant Data Retrieval: Through secure retrieval-augmented generation (RAG), manufacturers can safely enhance AI model performance while maintaining strict control over sensitive production data and intellectual property. This capability is particularly crucial for organisations balancing AI innovation with industrial security requirements.

Enhanced Governance and Compliance: The platform’s robust governance framework helps manufacturers:

• Enforce strict data governance policies across industrial AI implementations
• Maintain detailed audit logs of all AI interactions with production data
• Ensure compliance with both the Code of Practice and industrial regulations
• Monitor and report on AI data access patterns in manufacturing settings

Real-Time Protection: Comprehensive encryption and real-time access tracking provide the continuous monitoring and protection required by the Code, enabling manufacturers to:

• Protect intellectual property and production data throughout its lifecycle
• Track and control AI system access to operational technology
• Respond rapidly to potential security incidents
• Maintain detailed compliance documentation for regulatory requirements

Through these capabilities, Kiteworks helps manufacturing organisations achieve the delicate balance between enabling AI innovation and maintaining the strict data protection standards required by the Code of Practice while ensuring continuous, efficient production operations.

With the Kiteworks Private Content Network organizations protect their sensitive content from AI risk with a zero trust approach to Generative AI. The Kiteworks AI Data Gateway offers a seamless solution for secure data access and effective data governance to minimize data breach risks and demonstrate regulatory compliance. Kiteworks provides content-defined zero trust controls, featuring least-privilege access defined at the content layer and next-gen DRM capabilities that block downloads from AI ingestion.

With an emphasis on secure data access and stringent governance, Kiteworks empowers you to leverage AI technologies while maintaining the integrity and confidentiality of your data assets.

To learn more about Kiteworks and protecting your sensitive data from AI ingestion, schedule a custom demo today.

Additional Resources

• Blog Post Kiteworks: Fortifying AI Advancements with Data Security
• Press Release Kiteworks Named Founding Member of NIST Artificial Intelligence Safety Institute Consortium
• Blog Post US Executive Order on Artificial Intelligence Demands Safe, Secure, and Trustworthy Development
• Blog Post A Comprehensive Approach to Enhancing Data Security and Privacy in AI Systems
• Blog Post Building Trust in Generative AI with a Zero Trust Approach