Sentinel

AI and Data Security Governance

Sentinel

Sentinel is our specialized security and compliance framework for the entire AI lifecycle. It provides a multi-layered audit for data pipelines, AI/ML models, and autonomous agents, identifying vulnerabilities before they reach production.

With robust static analysis, data fingerprinting, and runtime monitoring, Sentinel provides the technical guardrails and verifiable auditing required to secure your most critical data and AI assets.

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 behavior, ensuring your models remain accurate, compliant, and reliable.

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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.

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.

Sentinel is a core component of a modern compliance stack. It provides the automated checks and verifiable evidence required to prove adherence to regulations like GDPR, HIPAA, and PCI. When combined with our Enclave framework, it provides a complete, auditable system for identifying security gaps and demonstrating that data governance and privacy policies are being continuously enforced across all cloud workflows.

Sentinel's auditing capabilities are platform-agnostic, allowing you to secure and monitor both cloud-native and legacy hybrid environments. It provides a consistent security and compliance posture across your entire infrastructure, ensuring all data workflows, regardless of where they run, are secure and auditable.

Why It Works

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.

This framework provides the continuous, automated evidence required for regulatory audits. It replaces manual, time-consuming compliance checks with an automated, auditable system that aligns with standards like OWASP and GDPR. This simplifies adherence to complex regulations and proves your data is being handled securely.

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 Code & Library Analysis

Performs multi-layered static analysis (SAST) on your source code and third-party libraries to identify security vulnerabilities, insecure coding patterns, and encryption misconfigurations.

Dependency & Config Auditing

Scans dependencies and configuration files (YAML, JSON, etc.) to detect vulnerabilities, misconfigurations, hardcoded secrets, and insecure endpoints.

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.

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.

Network Activity Monitoring

Flags potentially unsafe network activities defined in your code, such as hardcoded IPs or unauthorized external API calls, to mitigate the risk of data exfiltration.

Standards-Aligned Auditing

Aligns all security analysis and vulnerability reporting with industry standards like OWASP and IEEE, delivering actionable, prioritized insights for your security teams.

Sentinel

Data Fingerprinting and AI Drift Detection

Common Use Cases

Why It Works

A Multi-Layered Framework for AI Security

Static Code & Library Analysis

Dependency & Config Auditing

Data Fingerprinting & Lineage

AI Model Drift Detection

Agentic AI Runtime Governance

Verifiable Audit Trails

Network Activity Monitoring

Standards-Aligned Auditing

Built for Cloud. Ready for AI.