Security Review for OT/IT AI Assistants

• CISO or Head of OT Security
• Head of Digital and Industry 4.0
• OT/IT Architect
• AI Product Owner

Utilities, mines and plants are piloting AI assistants that summarise alarms, answer questions from OEM manuals, draft work orders and query historian data. These assistants sit close to the IT/OT boundary and take instructions from the text they read, which creates risks a conventional VAPT does not cover.

We run a structured security review of the assistant before it reaches plant users, and again after significant changes. We map its data flows, tools and identities against your OT zones, then build test playbooks for your setting: direct and indirect prompt injection, jailbreaks aimed at unsafe procedures, leakage of network diagrams, credentials and personal data, and misuse of every tool the assistant can call. Our multi-agent test framework runs these playbooks against a staging instance, never live control systems. Security engineers validate each finding, record reproduction steps and evidence, propose fixes and retest once you have remediated.

• Architecture and data-flow review against your IT/OT zones and conduits
• Direct and indirect prompt injection tests through manuals, work orders, emails and tickets
• Unsafe-procedure tests such as skipping isolation, bypassing interlocks or silencing alarms
• Tool and permission review for least privilege and read-only access to OT data
• Leakage tests for network diagrams, credentials, plant drawings and personal data
• Evidence pack mapped to OWASP Top 10 for LLM Applications, NIST AI RMF and IEC 62443 concepts

1. 01Agree scope and rulesWe agree the assistant, environment, test windows and rules of engagement with your OT and IT security teams.
2. 02Map data flows and toolsWe document what the assistant reads, which tools it can call, which identities it uses and where it crosses zone boundaries.
3. 03Run test campaignsThe test framework runs adversarial playbooks against the staging instance and records every prompt, response and tool call.
4. 04Validate findingsA security engineer reproduces each candidate finding and assesses its real impact.
5. 05Report with fixesFindings are reported with severity, evidence and specific remediation such as permission changes, input filtering or prompt and retrieval fixes.
6. 06RetestAfter remediation we rerun the relevant playbooks and close findings with evidence.

1. Scoping
   • Asset and data-flow inventory
   • Threat model for the assistant
   • Rules of engagement
2. Test framework
   • Attack playbook library
   • Multi-agent test orchestrator
   • Staging connectors with synthetic plant data
3. Evaluation
   • Response and policy classifiers
   • Tool-call monitor
   • Human validation by security engineers
4. Reporting
   • Findings register
   • Evidence pack with framework mapping
   • Retest tracker

Security engineers validate every finding before it is reported, and testing stays within the agreed rules of engagement and staging scope. Your team decides which risks to fix, accept or transfer.

• LLM platforms such as Azure OpenAI, AWS Bedrock or self-hosted models
• Staging copies of historian and CMMS connectors
• SIEM for log review
• Jira or ServiceNow for findings tracking
• Identity provider for test accounts

• No testing against live control systems; staging or replica environments only
• Signed rules of engagement before any test
• Synthetic plant data in test documents
• No destructive payloads or persistence
• Findings shared only through agreed secure channels

• Findings can be mapped to IEC 62443 concepts, OWASP Top 10 for LLM Applications and NIST AI RMF to support your OT security programme.
• Helps power-sector entities evidence risk assessment of new digital systems under CEA cyber security guidelines.

• Energy, Mining & Utilities

  Technical Report Search Assistant

  A search assistant over geological, engineering and incident reports that answers engineers' questions with page-level citations and respects existing document permissions.

• Manufacturing

  Security Review for Plant and Enterprise AI Copilots

  Test copilots that touch SAP, CMMS, drawings and costings for prompt injection, data leakage and unsafe tool access before they reach plant users, with validated findings and fixes.

• Fintech & Payments

  AI Agent Permission and Tool-Use Security Review

  Find out what your AI agents can actually do: which tools, credentials and money-moving actions they reach, where approvals sit, and whether injected text can make them misuse tools.

• SaaS & AI Product Companies

  Continuous AI Security Regression in CI

  Run versioned prompt-injection, jailbreak and data-leakage suites in CI on every prompt, model, tool or index change, and block releases that regress past agreed severity thresholds.