Professional Cyber Security Services
A security and safety review of the AI models, analytics and automated decision logic that now influence how your physical processes run.
Industrial organizations are adding machine learning and automation to operations at a fast pace: predictive maintenance models, process optimization, computer vision for quality inspection, anomaly detection, digital twins and, increasingly, AI agents that recommend or make setpoint changes. These systems can improve output and reliability, but they also introduce a new way for things to go wrong.
A model fed with manipulated sensor data can recommend unsafe setpoints. An optimization engine can push equipment outside its design limits. An automation workflow with excessive permissions can become a path for an attacker to influence the process. The OT Model & Automation Safety Review examines these risks before they turn into incidents.
We assess how sensor, historian, and external data flow into models, and whether an attacker or a faulty instrument could feed data that changes model behavior in unsafe ways.
We test how models respond to edge cases, drift, missing data and adversarial inputs, and whether their outputs stay within safe operating limits.
We review what automated systems and AI agents are allowed to change, whether hard limits and interlocks sit outside the model, and whether a human approves high-consequence actions.
We examine the connections between AI platforms, cloud services, historians and control systems, including credentials, APIs and data diodes, for paths an attacker could use to reach the process.
We review where models, libraries and vendor analytics come from, how updates are delivered and whether changes are tested before they reach production.
We check whether model decisions are logged, whether anomalies in model output are detected, and whether operators can fall back to manual or conventional control quickly.
The review brings together our AI security and OT teams. We work from architecture documentation and interviews with data scientists, control engineers and operations staff, then test models and automation logic in development or simulated environments rather than on live processes. Findings are assessed against process safety principles as well as security frameworks, including NIST AI RMF and ISO/IEC 42001 where relevant.
We inventory the models, analytics and automation in scope and map how they connect to operational data and control systems.
We combine security threat modeling with process hazard thinking to identify how manipulation or failure could lead to unsafe or costly outcomes.
We test data pipelines, model behavior and automation controls in a non-production environment.
We deliver prioritized findings and design recommendations for guardrails, monitoring and governance.
Inventory of AI models and automation influencing operations, with their level of authority
Threat and hazard scenarios for each high-consequence use case
Test results covering data manipulation, model robustness and access paths
Recommended engineering guardrails, independent limits and human approval points
Governance recommendations for model change management and monitoring
Our risk reduction strategy melds unparalleled technical acumen with a client-focused approach to deliver targeted, cost-effective, and accessible solutions that fortify your organization against the ever- evolving cyber threat landscape.
We leverage our cybersecurity expertise to safeguard your business integrity, ensuring you operate securely, move forward confidently, and build trust in an interconnected digital world.
We deploy cutting-edge cybersecurity measures and personalized strategies to offer unwavering data protection, reinforcing our commitment to preserving your company’s invaluable digital assets.
Reach out to us today and discover the potential of bespoke cybersecurity solutions designed to reduce your business risk.