Corporate boards need to understand how artificial intelligence is changing their organisation’s overall risk profile, rather than receiving a technical list of potential AI threats, according to a recent analysis published by TechTarget.
The analysis argues that AI does not automatically increase or decrease risk. Its effect depends on how an organisation uses the technology, where it is deployed and what controls surround it. For example, an AI customer-service chatbot could introduce the risk of inaccurate or hallucinated responses while simultaneously reducing customer dissatisfaction caused by long waiting times.
This makes AI risk particularly difficult for boards to assess. The focus needs to move from simply identifying technical vulnerabilities towards understanding how AI changes the organisation’s security, financial, operational and reputational risk profile.
Understanding Actual AI Use
A key requirement is knowing how AI is actually being used across the organisation.
This includes customer-facing applications, internal business processes and shadow adoption, where employees or departments use unapproved AI tools without formal organisational oversight. Organisations must also consider indirect AI adoption, where technology vendors and service providers incorporate artificial intelligence into products already being used by the business.
Security teams therefore need visibility into both current AI usage and planned future adoption.
The technical assessment should cover how models generate outputs, how context is constructed and what additional software or systems extend the model’s capabilities. However, the depth of technical analysis should correspond to the actual use case.
An organisation using a commercial large language model for basic document formatting presents a different risk profile from one developing its own models or deploying autonomous artificial-intelligence systems for IT operations.
From Technical Risk to Board-Level Risk
The analysis recommends converting technical findings into a clear risk narrative that boards can understand and act upon.
Rather than presenting directors with extensive technical terminology, security leaders should explain:
- How AI is affecting the organisation specifically
- Whether the overall risk profile is increasing or decreasing
- Where additional investment or controls are required
- How the risk profile is expected to change
- What management is doing to address the identified risks
This approach allows boards to focus on decisions rather than technical detail.
The analysis also recommends using established risk frameworks and resources, including the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework, its supporting guidance, MITRE ATLAS and the Open Worldwide Application Security Project’s lists covering large language models and agentic systems.
AI Risk Is a Moving Target
One of the central challenges is that AI risk does not remain static.
AI models change, organisations introduce new applications, employees adopt new tools and vendors incorporate additional AI capabilities into existing products. Consequently, a risk assessment conducted at the time of initial deployment may not remain adequate months later.
Continuous monitoring of AI adoption is therefore becoming important for enterprise risk management.
For banks and insurers, this is particularly relevant. AI may be used in credit assessment, fraud detection, underwriting, claims, customer service, compliance, cybersecurity and operational processes. Each application can create a different combination of model, data, conduct, operational and regulatory risks.
Boards consequently need visibility not simply into the number of AI systems being used, but into which business-critical processes depend on them and how their risk profiles are changing.
Governance Needs Business Context
The broader message is that effective AI governance cannot remain solely within the technology or cybersecurity function.
Business teams, risk management, internal audit, compliance, legal, cybersecurity and senior management all have roles in understanding how AI is being adopted and how its risks should be managed.
For boards, the objective is not to treat AI as inherently dangerous or inherently safe. Instead, they need to understand where the organisation’s risk is moving, by how much and over what time horizon.
This approach can help organisations avoid two equally problematic responses: ignoring emerging AI risks or responding to them with unnecessary restrictions that do not reflect the actual risk.
As AI adoption expands, the ability to translate technical developments into financial, operational, reputational and strategic risk information will become an increasingly important responsibility for senior management and boards.
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