AI & Data

AI Discovery

A robust AI Discovery capability automatically identifies, inventories, and maps all AI assets across environments — including models, agents, applications, datasets, and dependencies — to eliminate blind spots in governance and security. It provides a continuously updated, inventory with ownership, lineage, and risk metadata so teams can see “shadow AI,” understand relationships between assets, and align deployments with compliance frameworks. By establishing visibility of AI throughout development and production, it lays the foundation for effective risk management, governance, and secure scaling of AI initiatives.

AI Guardrails

Detect and stop AI attacks in real time to protect agents, models, and AI-powered workflows from prompt injection, jailbreak attempts, unsafe outputs, data exfiltration, and malicious tool misuse—before harm occurs. As AI systems become more autonomous and integrated into enterprise operations, their attack surface expands. Threat actors can manipulate prompts, override safeguards, exploit tool integrations, or trick agents into leaking sensitive data or executing unintended actions. Static guardrails and periodic reviews are not enough. Protection must be continuous, adaptive, and embedded directly into runtime environments.

AI Attack Simulation

AI attack simulation continuously tests and strengthens the security of AI systems by simulating real-world adversarial threats before they can be exploited. It automates red-teaming and adversarial testing across prompts, models, and agentic workflows to uncover vulnerabilities such as prompt injection, data leakage, and policy violations, then provides actionable insights and hardening recommendations. By validating security policies, probing for extraction risks, and continuously exercising defenses as models evolve, this capability helps organisations proactively identify weaknesses early, improve resilience against emerging AI threat vectors, and integrate security into the AI development and deployment lifecycle.

AI Supply Chain Risk Management

A structured AI supply risk management ensures that models and AI components entering your environment are trustworthy and free from hidden risks by scanning proprietary, third-party, and open-source models for vulnerabilities, tampering, malware, and backdoors before they reach production. It analyzes model architecture, layers, weights, and dependencies to detect anomalies, tracks model lineage and provenance to understand origins and relationships, and generates an AI Bill of Materials to support governance and risk management. By verifying model integrity, enforcing governance controls, and continuously measuring security posture, this capability helps organisations reduce exposure to unsafe or compromised AI.

Data Security for AI

A GenAI (Generative AI) data security helps organisations enable generative AI safely by discovering and classifying sensitive data, monitoring AI usage, and preventing data leakage or unauthorized sharing of sensitive information. It gives visibility into “shadow GenAI” use and governs interactions with both public and enterprise AI tools by blocking or masking sensitive inputs and enforcing access controls. The approach combines context-aware intelligence to protect data across public AI services, AI assistants, and proprietary models while maintaining compliance and reducing risk, balancing innovation with robust data protection.

Data Security Posture Management

A Data Security Posture Management (DSPM) capability gives organisations comprehensive visibility into where sensitive data resides, what it contains, who can access it, and how it’s being used, across cloud and on-premises environments. It goes beyond simple discovery by automatically classifying structured and unstructured data with context-aware AI, continuously assessing risk, monitoring security posture in real time, and enabling remediation actions to reduce exposure. By unifying discovery, contextual classification, risk prioritisation, and remediation workflows, DSPM helps teams eliminate blind spots, strengthen governance, support compliance, and proactively protect critical data throughout its lifecycle.