AI Security & Data Sovereignty

The AI-Specific Threat Landscape

AI systems are vulnerable to attack vectors that do not exist in conventional software. Understanding these threats is the foundation of an effective security strategy.

Key Threat Categories

• Prompt injection — Malicious inputs that manipulate model behaviour, bypassing safety controls or extracting sensitive information embedded in system prompts.
• Training data poisoning — Introducing corrupted data into training sets to create backdoors or bias model outputs in specific, attacker-controlled ways.
• Model extraction — Querying a model systematically to reconstruct its weights or training data, enabling intellectual property theft or vulnerability discovery.
• Data leakage — Sensitive information from training data or enterprise context surfacing in model outputs, potentially violating privacy regulations or exposing trade secrets.
• Supply chain attacks — Compromised model weights, corrupted open-source libraries, or malicious plugins that introduce vulnerabilities into the AI pipeline.
• Adversarial inputs — Carefully crafted inputs that cause models to produce incorrect outputs while appearing legitimate to human observers.

Defence-in-Depth for AI Systems

Securing AI requires a layered approach that addresses vulnerabilities at every level of the stack, from data ingestion to model output.

Layer 1: Data Security

• Classify all training and inference data according to sensitivity levels.
• Implement data access controls based on the principle of least privilege.
• Encrypt data at rest and in transit, including model weights and embeddings.
• Maintain data lineage records for all training datasets.
• Implement automated PII detection and redaction in data pipelines.

Layer 2: Model Security

• Validate and scan all model artifacts before deployment, including third-party models.
• Implement model signing and integrity verification to prevent tampering.
• Apply rate limiting and anomaly detection on inference endpoints.
• Use differential privacy techniques where appropriate to protect training data.
• Regularly test models against known adversarial attack patterns.

Layer 3: Application Security

• Implement input validation and sanitisation to mitigate prompt injection attacks.
• Apply output filtering to prevent sensitive data leakage in model responses.
• Use role-based access controls for all AI endpoints and management interfaces.
• Log all interactions for security monitoring and forensic analysis.
• Deploy content safety classifiers to detect harmful or policy-violating outputs.

Layer 4: Infrastructure Security

• Isolate AI workloads in dedicated compute environments with network segmentation.
• Implement secure model serving with TLS termination and API gateway controls.
• Use immutable infrastructure patterns for model deployment.
• Maintain separate environments for training, testing, and production.

Data Sovereignty for AI

Data sovereignty ensures that data is subject to the laws and governance structures of the jurisdiction where it resides. For AI systems, sovereignty considerations extend beyond storage to processing, model training, and inference.

Key Sovereignty Principles

• Data residency — Ensure training data and inference data remain within jurisdictional boundaries as required by regulation (GDPR, sector-specific rules).
• Processing sovereignty — Model training and inference must occur in approved locations, which may require on-premises or sovereign cloud deployment.
• Vendor sovereignty — Understand and control which third-party providers have access to your data through API calls, logging, or model improvement programmes.
• Exit sovereignty — Maintain the ability to migrate AI workloads between providers without losing data or capability.

Sovereignty Compliance Checklist

• Map all data flows in your AI pipeline, including third-party API calls.
• Verify that model providers do not use your data for training without explicit consent.
• Implement data residency controls at the infrastructure level.
• Maintain contractual provisions for data processing agreements with AI vendors.
• Regularly audit data flows against sovereignty requirements.
• Consider on-premises or sovereign cloud deployment for the most sensitive workloads.