AI Risk Assessment
Artificial intelligence can support faster decisions, improve productivity, automate routine work, and create new services. It can also introduce risks that are difficult to recognise before a system is used in real business situations.
An AI system may generate inaccurate information, treat individuals unfairly, expose sensitive data, or behave differently when its model, data, or operating environment changes. The risk increases when AI influences important decisions, interacts directly with customers, or performs actions within connected business systems.
An AI Risk Assessment from CA helps your organisation understand the risks associated with a proposed or existing AI system. We examine the business purpose, data, model, users, suppliers, technical environment, and potential consequences to identify where controls are needed and whether the remaining risk is acceptable.
Why This Matters
AI risk cannot be understood by looking at the model alone. The same technology can create very different levels of risk depending on how it is used, which information it processes, who relies on its outputs, and what happens when it makes a mistake.
A tool that helps an employee prepare an internal draft may require relatively simple safeguards. An AI system that assesses applications, recommends medical or financial actions, prioritises employees, or communicates directly with customers requires more detailed evaluation and oversight.
A structured AI risk assessment helps you:
Identify risks before deployment
Recognise possible problems involving accuracy, security, privacy, fairness, transparency, reliability, and regulatory compliance before the system becomes part of normal operations.
Apply proportionate controls
Select safeguards that reflect the purpose, level of automation, affected individuals, and possible consequences of the AI system.
Support informed approval decisions
Give system owners and management the information they need to approve, restrict, change, postpone, or reject an AI use case.
Create reliable evidence
Document the intended use, identified risks, implemented controls, responsible owners, and reasons behind important decisions.
Our Approach
We do not evaluate AI risk through a generic questionnaire that treats every system in the same way. A meaningful assessment must reflect the business process, information, users, technical design, and real consequences of failure.
Our approach considers the complete AI solution rather than only the underlying model. This includes data sources, prompts, applications, integrations, human decisions, external providers, access controls, monitoring, and the way outputs are used.
The depth of the assessment is proportionate to risk. Lower-impact use cases can follow a simpler process, while systems affecting individuals, important services, sensitive information, or automated decisions receive more detailed examination.
Define the AI system and its intended purpose
We begin by understanding what the AI system is expected to do, which business need it addresses, and how its outputs will be used.
We identify the users, affected individuals, data sources, models, applications, suppliers, integrations, and business processes involved. We also establish whether the system provides advice, creates content, supports a human decision, or performs an action automatically.
The intended use and reasonable limits of the system are documented clearly. We also consider possible uses that were not originally planned but could realistically occur after deployment.
This creates a reliable foundation for the assessment and helps prevent the system from gradually being used for higher-risk purposes without appropriate review.
Identify and analyse relevant risks
We examine the ways in which the AI system could cause harm, fail to meet expectations, or conflict with the organisation’s obligations.
The assessment may cover inaccurate or misleading outputs, unfair treatment, inappropriate profiling, privacy concerns, confidential data exposure, cybersecurity threats, intellectual property issues, insufficient transparency, operational dependency, and misuse by employees or external users.
We also consider risks created by training data, business information, prompts, connected tools, external models, and third-party services. Dependencies on individual providers are examined, including what may happen if a model changes, becomes unavailable, or no longer meets the organisation’s requirements.
Each risk is considered in relation to its likelihood, possible impact, affected parties, existing safeguards, and the organisation’s ability to detect and respond to a problem.
Define controls and evaluate remaining risk
We identify safeguards that can reduce the recognised risks to an appropriate level.
These may include human review, access restrictions, data minimisation, testing requirements, approval processes, output verification, supplier controls, user guidance, logging, security testing, fallback procedures, and limitations on how the system can be used.
Controls are connected to specific risks and assigned to clear owners. We define what each control should achieve, how its effectiveness will be checked, and what evidence should be maintained.
After considering the proposed controls, we evaluate the remaining risk. Where the risk remains too high, further safeguards, changes to the system, a narrower use case, or a decision not to proceed may be necessary.
Document the decision and establish ongoing review
We record the assessment results in a clear format that can be understood by business, technical, security, privacy, legal, compliance, and management teams.
The documentation describes the intended use, affected parties, key risks, selected controls, remaining risk, responsible owners, review arrangements, and final approval decision.
AI risks can change after deployment. Models are updated, data develops, new users adopt the service, and the system may begin supporting additional processes. We therefore define conditions that should trigger a new assessment or formal review.
These may include a material model change, a new data source, expanded automation, use with a different group of individuals, a significant incident, repeated complaints, or a change in regulatory requirements.
What Sets CA Apart
Assessment based on actual use
We evaluate how the AI system will operate in a real business process rather than assessing the model in isolation.
Risks considered from several perspectives
We bring together business, technical, security, privacy, legal, ethical, operational, and regulatory considerations.
Controls proportionate to potential impact
We apply a practical level of assessment and oversight based on the importance and possible consequences of the use case.
Clear support for decision-makers
We explain the risks, controls, uncertainties, and remaining exposure so that responsible leaders can make an informed decision.
The Business Value
An AI Risk Assessment from CA gives your organisation a structured and defensible basis for deciding whether and how an AI system should be used.
Business owners gain a clearer understanding of the risks connected to the intended outcome. Technical teams receive practical requirements for designing, configuring, testing, and operating the solution. Management receives the information needed to approve the use case and accept any remaining risk.
Early assessment can also prevent costly corrections later. Problems involving data, model selection, access, transparency, suppliers, or human oversight can be addressed before the solution becomes widely used or deeply integrated into business processes.
Documented decisions and clearly assigned controls provide stronger evidence for internal governance, customer assessments, audits, and regulatory reviews.
The result is more confident AI adoption, better-informed investment decisions, and a lower likelihood that an AI initiative creates unexpected harm, disruption, or compliance problems.
Next Step
AI risk is easier to manage when it is considered before the system becomes part of an important business process.
Contact CA for an initial consultation. We will help you define the assessment scope, understand the risks associated with your AI use case, and identify practical controls that support a responsible decision.