This section supports Principle 3: You know how to use AI securely.

Cyber security is a primary concern for all government services per the Government Cyber Security Strategy. When building and deploying AI systems, government has responsibility to ensure these are secure and resilient to cyber attacks.

Core Requirement: Your service must comply with government's Secure by Design principles before deployment.

AI-Specific Risks: Some security risks apply uniquely to AI and/or generative AI technologies.

Cross-Government Support: Join the cross-government AI security group bringing together security practitioners, data scientists and AI experts (GOV.UK email addresses only).

Depending on how AI systems are used, they present different security challenges and varying risk levels that must be managed.

Simple way to implement AI using publicly available commercial applications (Google Gemini, ChatGPT).

Security Considerations:

• Cannot easily control data input to models
• Must rely on educating users on appropriate/inappropriate data entry
• No control over model outputs
• Subject to commercial license agreements and privacy statements
• Example: OpenAI uses ChatGPT prompt data to improve models (users can opt out)

Key Restriction: Must not enter official information unless published or cleared for publication.

Many vendors include AI features directly within products (Slack GPT, Microsoft Copilot).

Unique Concerns:

• Each application has own security concerns
• Important to understand underlying architecture
• Know what mitigations vendor has implemented for inherent AI risks

AI-Powered Plugins/Extensions:

• Many community-built extensions with AI capabilities
• Example: Visual Studio Code ecosystem
• Require careful security assessment before adoption

Advantages:

• More control over inputs and outputs compared to public applications
• Can implement additional security controls
• Better integration with existing systems

Considerations:

• Still subject to vendor security measures
• Need to understand API security implications
• Implement proper authentication and authorization
• Monitor API usage and potential abuse

Benefits:

• Full control over data processing
• Can implement custom security measures
• No dependence on external services
• Better compliance with data protection requirements

Challenges:

• Requires significant technical expertise
• Infrastructure and maintenance costs
• Responsibility for all security aspects
• Need to stay updated with model security patches

Key Security Requirements:

• Ensure proper data classification and handling
• Implement appropriate access controls
• Monitor data usage and potential leakage
• Comply with organizational data policies
• Consider data residency requirements
• Implement audit trails for data processing

Open-Source Models:

• Transparency in model architecture and training
• Community security review
• Customization possibilities
• No vendor lock-in

Closed-Source Models:

• Vendor-managed security
• Professional support
• May have proprietary security features
• Less transparency in operation

Data Poisoning

• Malicious modification of training data
• Can cause model to behave incorrectly
• Particularly concerning for models trained on public data
• Mitigation: Careful data curation and validation

Perturbation Attacks

• Small, carefully crafted changes to inputs
• Cause AI systems to make incorrect predictions
• Often imperceptible to humans
• Mitigation: Robust model training and input validation

Prompt Injections

• Malicious prompts designed to manipulate AI behavior
• Can cause AI to reveal sensitive information
• Bypass intended restrictions or safeguards
• Mitigation: Input sanitization and output filtering

Hallucinations

• AI generating false but plausible-sounding information
• Can lead to misinformation spread
• Particularly problematic in generative AI
• Mitigation: Output verification and fact-checking systems

Phishing Enhancement

• AI can create more convincing phishing emails
• Personalized attacks using available information
• Scale attacks more effectively
• Mitigation: Enhanced email filtering and user training

Cyber Attack Sophistication

• AI can automate attack reconnaissance
• Generate more effective attack strategies
• Adapt attacks in real-time
• Mitigation: AI-powered defense systems

Social Engineering

• AI-generated deepfakes for impersonation
• Sophisticated conversation bots for manipulation
• Analysis of social media for targeted attacks
• Mitigation: Verification procedures and awareness training

Data Leakage

• Models may inadvertently reveal training data
• Personal information exposure through model outputs
• Sensitive organizational data revelation
• Mitigation: Data anonymization and output filtering

Model Inversion Attacks

• Attempts to reconstruct training data from model behavior
• Can reveal sensitive information about individuals
• Particularly concerning with smaller datasets
• Mitigation: Differential privacy and model protection

Membership Inference Attacks

• Determining if specific data was used in training
• Privacy violation for individuals
• Regulatory compliance issues
• Mitigation: Privacy-preserving training techniques

AI also provides opportunities to enhance security posture.

Automated Threat Identification

• AI can analyze large volumes of security data
• Identify patterns indicative of threats
• Faster detection than manual analysis
• Real-time threat response capabilities

Behavioral Analysis

• Detect anomalous user behavior
• Identify potential insider threats
• Network traffic analysis for intrusions
• Application usage monitoring

Automated Response Systems

• Immediate response to detected threats
• Reduced response time
• Consistent application of security policies
• Escalation of complex incidents to humans

Forensic Analysis

• Automated log analysis
• Pattern recognition in incident data
• Timeline reconstruction
• Evidence correlation across systems

Dynamic Risk Assessment

• Continuous evaluation of security posture
• Adaptation to changing threat landscape
• Personalized security measures
• Context-aware access controls

Predictive Security

• Anticipate potential security issues
• Proactive vulnerability management
• Threat intelligence analysis
• Security resource optimization

Security by Design

• Incorporate security from project inception
• Consider security implications of AI model choice
• Plan for secure data handling throughout lifecycle
• Design incident response procedures

Risk Assessment

• Identify specific AI security risks for your use case
• Assess potential impact and likelihood
• Consider both technical and organizational risks
• Document risk mitigation strategies

Secure Development Practices

• Use secure coding standards
• Implement input validation and sanitization
• Regular security testing throughout development
• Code review with security focus

Model Security

• Secure model training pipelines
• Protect model parameters and architecture
• Implement model versioning and integrity checks
• Consider model encryption for sensitive applications

Infrastructure Security

• Secure deployment environment
• Implement proper access controls
• Network segmentation for AI services
• Regular security monitoring and logging

Operational Security

• Continuous monitoring of AI system behavior
• Regular security assessments and updates
• Incident response procedures specific to AI
• User training on AI security best practices

Continuous Monitoring

• Real-time monitoring of AI system outputs
• Anomaly detection in system behavior
• Performance degradation that might indicate attacks
• User interaction monitoring for abuse

Regular Updates

• Keep AI models and supporting software updated
• Apply security patches promptly
• Regular re-evaluation of security measures
• Update threat models as AI technology evolves

• Assess current AI implementations for security risks
• Implement basic security controls (authentication, authorization, encryption)
• Educate users on AI-specific security risks
• Establish AI security incident response procedures

• Implement AI-specific security monitoring
• Develop policies for AI data handling
• Conduct security assessments of AI vendors
• Join cross-government AI security community

• Develop comprehensive AI security framework
• Invest in AI security expertise and tools
• Implement advanced AI security technologies
• Regular review and update of AI security measures

• Ensure AI security measures align with organizational policies
• Document AI security procedures and controls
• Regular security audits of AI systems
• Maintain compliance with relevant security standards

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