Sentinel

AI and Data Security Governance

Continuous AI Risk Coverage

Sentinel is our specialised security framework for the entire AI lifecycle, from data pipelines and AI/ML models through to autonomous agents in production. It identifies and contains AI-specific risk before it reaches operations, and produces the evidence stream that downstream compliance work (including our Praxis solution) depends on.

With static analysis tuned to AI and agent code, data fingerprinting, drift detection, and runtime policy-as-code, Sentinel provides the technical guardrails required to secure your data and AI assets at the same speed your platform and agents operate.

Data Fingerprinting and AI Drift Detection

Sentinel's data fingerprinting tracks data lineage and validates integrity. Its drift detection capabilities continuously monitor for deviations in both your data and your AI model's behaviour, ensuring your models remain accurate and reliable as your platform and the data feeding it evolve.

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Common Use Cases

This is the critical security layer for autonomous systems. Sentinel is engineered to govern AI agents by providing real-time runtime monitoring and policy-as-code (PaC) enforcement. It audits the agent's available tools, actively scans inputs for prompt injection, and creates a verifiable, immutable log of every decision and action. This framework provides the essential, non-negotiable guardrails required to deploy autonomous agents safely in production.

AI-driven security tooling surfaces candidate vulnerabilities and exploit attempts at machine speed, far faster than human triage can keep up. Sentinel's Proof-Point capability sits between any AI discovery layer and your SecOps team, validating each finding inside an instrumented, IaC-synchronised replica of your production environment with the deployed defences in place. Findings that cannot be proven exploitable are deprioritised with auditable reasoning. Findings that can are routed directly into your remediation pipeline. Human attention lands only on the candidates that actually warrant it.

Sentinel provides a comprehensive, continuous audit for the entire AI/ML lifecycle. This includes scanning models for known security vulnerabilities, validating compliance with data governance policies, and mitigating the risk of sensitive data leakage from training sets. It establishes a verifiable chain of custody for model integrity and provides continuous monitoring to detect unauthorized access or anomalous behavior.

We use advanced data fingerprinting to track your data's lineage from source to model, ensuring cryptographic proof of its integrity at every step. Sentinel actively monitors data pipelines for unauthorized changes, statistical anomalies, or data quality degradation. This is critical for maintaining the reliability and trustworthiness of the production AI models that are entirely dependent on that data.

Our Approach

This "Shift-Left" approach uses multi-layered static analysis to scan source code, libraries, and configurations before deployment. This identifies vulnerabilities, hardcoded secrets, and misconfigurations early in the development lifecycle, preventing them from ever reaching production.

This is more than just security; it’s about trust. Sentinel’s data fingerprinting and drift detection capabilities provide a verifiable guarantee that your data is accurate and your models are reliable. It creates an immutable record of your data's lineage and alerts you the moment a model's behavior deviates, preventing corrupted data or stale models from impacting business decisions.

Sentinel is an AI security framework, not a compliance product. What it produces, continuous static analysis findings, runtime monitoring logs, immutable agent-action records, drift detection signals, exploitability validation results, is the substrate compliance work uses to demonstrate adherence to AI Act Article 15, GDPR Article 32, ISO 42001, NIST AI RMF, and OWASP AISVS. Our Praxis solution turns that evidence into regulator-ready packs.

Sentinel is not a new, disruptive platform you have to learn. It's a configurable, API-driven solution designed to integrate directly into your existing CI/CD pipelines and development workflows. This provides automated security and governance without slowing down your engineers or forcing them to change how they work.

What Sentinel Delivers

A Multi-Layered Framework for AI Security

Sentinel provides a comprehensive framework to secure your code, data, and AI models, from development to production.

Static Analysis for AI & Agent Code

Multi-layered static analysis tuned to AI model code, agent tool definitions, prompt templates, and orchestration logic. Identifies insecure patterns, hardcoded secrets, encryption misconfigurations, and unsafe network activities such as hardcoded IPs or unauthorised external API calls that could enable data exfiltration.

AI Stack Dependency & Config Audit

Scans AI/ML library dependencies (PyTorch, TensorFlow, Hugging Face, LangChain, agent frameworks), model configs, prompt configs, and policy bundles for vulnerabilities, misconfigurations, hardcoded secrets, and insecure endpoints before they reach production.

Data Fingerprinting & Lineage

Establishes a unique fingerprint for your critical datasets. This allows Sentinel to track and validate data lineage across your entire pipeline, ensuring data integrity and creating a verifiable chain of custody.

AI Model Drift Detection

Actively monitors production AI/ML models for both data and concept drift. This ensures models remain accurate and reliable, alerting you when their performance degrades or deviates from the intended use case.

Agentic AI Runtime Governance

Provides a runtime ‘firewall’ for autonomous agents. It monitors agent inputs for Prompt Injection and enforces Policy-as-Code (PaC) rules to block unauthorized or malicious actions before they can be executed.

Deterministic Exploitability Validation

The Proof-Point capability: each AI-surfaced security finding is validated inside an instrumented, IaC-synchronised replica of your production environment with deployed defences in place. Findings that cannot be proven exploitable are deprioritised with auditable reasoning, replacing alert fatigue with proven, prioritised remediation work.

Verifiable Audit Trails

Creates a secure, immutable audit log for all critical data and agent activities. This provides the verifiable evidence trail required for incident response and proving compliance to regulators.

AI Standards Alignment

Maps Sentinel’s findings, signals, and audit artefacts against OWASP AISVS, NIST AI Risk Management Framework, ISO/IEC 42001, and the technical expectations of the EU AI Act. Delivers prioritised insights against the standards your AI systems will be measured by.