A structured framework helping organizations identify, assess and manage AI-related risks before implementation, scaling and operational integration.
Many organizations evaluate the potential benefits of AI while underestimating operational, governance, compliance and vendor-related risks.
A structured risk register helps leadership teams understand where exposure exists, how controls should be implemented and which risks require ongoing monitoring.
The objective is not to eliminate innovation but to improve decision quality before significant investments are made.
Business disruption, process failures and implementation challenges.
Data quality, confidentiality, privacy and ownership concerns.
Unauthorized access, cyber threats and information leakage.
Regulatory obligations, legal exposure and governance failures.
Third-party dependency, service interruption and lock-in concerns.
Customer trust, brand impact and decision transparency.
| Risk | Impact | Likelihood | Control Strategy |
|---|---|---|---|
| Hallucinated Outputs | High | Medium | Human Review Process |
| Data Leakage | High | Medium | Access Controls & Policies |
| Vendor Lock-In | Medium | Medium | Alternative Supplier Strategy |
| Compliance Failure | High | Low | Governance Reviews |
| Cybersecurity Incident | High | Low | Security Controls |
| Poor Data Quality | Medium | High | Data Governance Framework |
Identify Risks
Assess Impact
Evaluate Likelihood
Define Controls
Monitor Continuously
Employees using unapproved AI tools without governance oversight.
Inability to explain AI-generated recommendations.
Sensitive information entering external AI environments.
Removing human oversight from critical business decisions.
Dependence on a single AI provider.
Controls becoming ineffective as systems evolve.
AI investments should be evaluated through both opportunity and risk lenses.
Organizations that establish structured risk management frameworks before implementation often reduce disruption, improve governance and increase long-term project success rates.
Risk management should evolve alongside AI adoption rather than being introduced after deployment.
Assess organisational readiness before adoption.
Establish oversight and accountability controls.
Evaluate vendor-related implementation risks.
Identify implementation risks, governance gaps and control requirements before scaling AI initiatives.
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