AI & Data
Security
AI & Data
Security
Securing AI and data from design to runtime and continuous protection mechanisms.
What We Do
SECURE DATA & AI BY DESIGN
Embed privacy, protection, and security controls from the earliest stages of AI and data solution design. Sensitive information is safeguarded through privacy-preserving techniques, threat modelling, and secure architectural patterns.
AI SUPPLY CHAIN PROTECTION
Secure AI models and dependencies through controls that protect pipelines, assets, and third-party integrations across the lifecycle.
QUALITY, ASSURANCE AND PERFORMANCE
Implement evaluation, observability, and performance monitoring that provide continuous visibility into models, agents, and data - ensuring systems deliver consistent, measurable results.
TRUST & SAFETY ENGINEERING
Embed governance frameworks, safety controls, and runtime protections that ensure AI systems operate securely, responsibly and in alignment with risk and compliance requirements.
Moving AI and data solutions into production introduces new risks that extend beyond traditional systems. Security must span design, development, deployment, and runtime operations. Without privacy-first design, secure supply chains, and continuous validation, organisations face increased exposure to data leakage, model compromise, and evolving threats that create operational and regulatory risk.
We work with enterprise security and engineering teams to embed protection across the AI lifecycle. From securing architectures and model supply chains to adversarial testing and runtime data protection, we ensure systems operate with resilience and trust. Through integration with leading security platforms and ecosystem partners, protection becomes continuous and aligned to enterprise risk - enabling organisations to scale AI securely and with confidence.
Delivering AI & Data Security
Privacy Preservation, Threat Modelling
- Privacy-preserving architecture design
- AI and data threat modelling assessments
- Sensitive data classification and risk mapping
- Secure data handling and privacy-by-design implementation
- AI solution risk scenario and attack surface analysis
- Security design patterns for AI and data workflows
Protect sensitive data from the outset through privacy-first design and proactive risk identification.
AI Supply Chain Security
- Model and dependency security validation
- AI pipeline and artifact integrity controls
- Third-party model and vendor risk assessments
- Secure model packaging and deployment controls
- AI component provenance and traceability implementation
- Supply chain risk monitoring across development environments
Secure AI models and dependencies across the lifecycle to reduce exposure to supply chain threats.
AI Pen Testing & Red Teaming
- AI and model penetration testing
- Adversarial testing against model behaviour
- Prompt injection and model exploitation testing
- AI system attack simulation exercises
- Red teaming of AI-enabled workflows and interfaces
- Vulnerability identification and remediation planning
Identify vulnerabilities early through adversarial testing that strengthens AI system resilience.
Data Masking & Context Protection
- Data masking and tokenisation implementation
- Context-aware data protection controls
- Secure data access and inference protection
- Sensitive data redaction and transformation
- Training and inference data protection strategies
- Confidential data exposure risk reduction
Safeguard sensitive information across training, inference, and operational workflows.
Our Approach
Embed privacy, protection, and security controls from the earliest stages of AI and data solution design.
Secure models, pipelines, and dependencies to protect AI assets and third-party integrations.
Identify vulnerabilities through penetration testing and red teaming that simulate real-world attack scenarios.
Connect AI capabilities into enterprise systems and workflows through scalable integration patterns.
Implement monitoring and protection mechanisms that evolve with changing risks and system behaviour, ensuring security remains active across runtime operations.
