Security & Compliance

Client data stays within your infrastructure or approved environments at all times. We never copy production data to our own systems. When we need data for development, we work with synthetic datasets, anonymised samples, or access your development environments through secure channels. All access is logged, time-limited, and revocable. For engagements involving sensitive data, we execute data processing agreements that specify exactly how data will be handled, who has access, and what happens at engagement end — including verified deletion of any temporary access credentials.

Yes. We design AI systems with SOC 2 controls mapped from the start — covering security, availability, processing integrity, confidentiality, and privacy. This includes implementing access controls with least-privilege principles, comprehensive audit logging for all model interactions, encryption at rest and in transit, change management processes for model updates, incident response procedures specific to AI failures, and monitoring for data leakage or unauthorised access. We provide evidence packages that your auditors can use directly and work with your compliance team to ensure controls align with your existing SOC 2 framework.

For healthcare clients, we implement the full spectrum of HIPAA technical safeguards. Protected health information is encrypted with AES-256 at rest and TLS 1.3 in transit. Access controls enforce minimum necessary access with role-based permissions and multi-factor authentication. All PHI access is logged in tamper-evident audit trails with 6-year retention. We implement automatic de-identification pipelines for data used in model training, and business associate agreements are executed before any engagement involving PHI. Model outputs are designed to never expose individual patient information.

We build GDPR compliance into the architecture rather than bolting it on after. This means implementing data minimisation — only processing personal data that is strictly necessary. We design systems with automated data subject request handling for access, rectification, erasure, and portability rights. Consent management is integrated at the data ingestion layer. For AI models, we implement techniques to prevent memorisation of personal data during training and provide mechanisms for model unlearning when erasure requests require it. Data protection impact assessments are conducted for high-risk processing activities.

We implement multi-layered defences. Input validation catches prompt injection, jailbreak attempts, and adversarial perturbations before they reach the model. Output filtering prevents data leakage, harmful content generation, and off-topic responses. We deploy guardrail models that evaluate both inputs and outputs in real time. Rate limiting and anomaly detection identify unusual usage patterns. For critical systems, we implement model watermarking to detect intellectual property theft and canary tokens to identify data exfiltration. Regular red-teaming exercises validate these defences against evolving attack techniques.

AI red teaming is a structured adversarial testing process where we attempt to break your AI system before bad actors do. This includes prompt injection attacks to bypass system instructions, jailbreak attempts to elicit harmful or off-policy outputs, data extraction attacks to recover training data or system prompts, denial-of-service scenarios through adversarial inputs, and social engineering attacks that exploit the model personality. We document findings in a detailed report with severity ratings, reproduction steps, and recommended mitigations. Red teaming should be conducted before every major release and periodically on production systems.

Every AI interaction generates a structured audit record containing the user identity and access context, full input with any pre-processing applied, model version and configuration used, complete output before and after any filtering, retrieval sources referenced for RAG systems, latency and token usage metrics, and any guardrail activations or policy violations. These records are stored in tamper-evident, append-only logs with configurable retention periods. We build dashboards for compliance teams to review interactions, search by user or content, and export evidence for regulatory inquiries or internal investigations.

We implement encryption at every layer. Data at rest uses AES-256 with keys managed through your KMS — AWS KMS, Azure Key Vault, or HashiCorp Vault. Data in transit uses TLS 1.3 for all API communication and mTLS for service-to-service communication within the AI platform. For particularly sensitive workloads, we leverage confidential computing with NVIDIA H100 Trusted Execution Environments that encrypt data during processing — meaning not even infrastructure administrators can access data while models are running inference. Key rotation is automated on a configurable schedule.

We align with established frameworks rather than inventing our own. The NIST AI Risk Management Framework provides our primary structure for identifying and mitigating AI-specific risks. ISO 42001 guides our AI management system practices. The EU AI Act risk classification informs our assessment of high-risk applications. We also incorporate OWASP guidelines for LLM security. For each engagement, we map applicable frameworks to concrete technical controls and operational procedures, creating a governance structure that satisfies multiple regulatory requirements simultaneously rather than treating each as a separate compliance exercise.

Bias mitigation starts with data audit — we analyse training data for demographic imbalances, historical biases, and representation gaps. During model evaluation, we run fairness metrics across protected attributes including disparate impact analysis, equal opportunity assessment, and calibration testing. For production systems, we implement ongoing monitoring that alerts when model behaviour diverges across user segments. When bias is detected, we have established remediation workflows: targeted data augmentation, model retraining with fairness constraints, or output post-processing. All bias assessments and remediation actions are documented for regulatory evidence.