AI, Data & Intelligence
AI Risk Management
Identification, assessment and treatment of risks from specific AI systems, including errors, bias, misuse, security, privacy and third-party dependency, documented in a way risk committees can act on.
Capability overview
What ai risk management involves
Every AI system carries its own risk profile. A demand forecast that is slightly wrong costs inventory; a triage model that systematically under-prioritises some patients causes harm. AI risk management assesses each system in its context, decides which risks are acceptable and puts controls in place for the rest.
Assessments follow the map, measure and manage functions of the NIST AI Risk Management Framework, with techniques from ISO/IEC 23894 on AI risk management, and for generative AI the NIST Generative AI Profile. Results feed your enterprise risk register so AI risk is reported alongside other operational risks rather than in isolation.
What is included
Risk areas assessed
Performance and error risk
How wrong outputs could occur, how often, who is affected and whether errors are noticed before they cause harm.
Fairness and discrimination risk
Potential for different outcomes across groups, especially where decisions affect access to credit, jobs, insurance or services.
Security and misuse risk
Exposure to manipulation, data extraction, prompt injection or use for purposes the system was not designed for.
Privacy and data risk
Lawful basis for data use, retention, re-identification risk and transfers to model providers.
Dependency and resilience risk
Reliance on external models or vendors, behaviour changes after updates and the fallback if the system is unavailable.
How we work
How we deliver ai risk management
Context mapping
Purpose, users, affected people, decisions supported and deployment environment documented.
Risk identification
Structured workshops with builders, business owners and control functions to list plausible failure scenarios.
Measurement
Where possible, risks quantified through testing such as error rates by segment or red team findings.
Treatment planning
Controls chosen for each material risk, with residual risk rated and accepted by the accountable owner.
Ongoing review
Key risk indicators monitored and assessments revisited after incidents, model changes or new uses.
Related capabilities
Related capabilities in Responsible AI & AI Security
AI Security Assessments
Independent security reviews of AI systems end to end, covering data pipelines, training infrastructure, model supply chain, inference APIs and integrations, mapped against MITRE ATLAS attack techniques.
AI Model Security
Protection of machine learning models themselves against theft, tampering, poisoning, adversarial inputs and malicious model files, from training through registry to production serving.
LLM Security
Runtime security for applications built on large language models: controls against prompt injection, sensitive data disclosure, insecure output handling, excessive agency and unbounded consumption.
AI Red Teaming
Adversarial exercises in which specialists try to make your AI system misbehave, including jailbreaks, harmful or biased outputs, data leakage and misuse of tools, before real users or attackers do.
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Where this applies
Healthcare & Life Sciences
Technology systems for regulated environments where privacy, auditability and continuity…
Manufacturing & Industrial
Connected operations, asset, field, supply chain and industrial platforms for complex operating…
Banking, Financial Services & Insurance
Technology systems for regulated environments where privacy, auditability and continuity…
E-Commerce
Digital platforms for customer experience, operations, commerce, content, marketing and service…
Questions & answers
Questions about AI Risk Management
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionAI systems can fail silently and probabilistically, change behaviour as data shifts and produce unfair outcomes without any technical fault. Those characteristics need additional assessment methods beyond standard IT risk controls.
No. A short screening questionnaire decides the depth. Full assessments are reserved for systems that influence significant decisions about people, money or safety.
The business owner of the process using AI owns the risk, supported by technical teams and overseen by risk, compliance and data protection functions.
A single system typically needs two to four weeks, depending on how much testing is required to measure risks rather than estimate them.
Next step
Discuss ai risk management with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.