Cyber security
AI can help attackers create convincing scams, phishing messages and deepfakes. AI systems can also create new attack points if access, monitoring, patching and testing are weak.
Workplace AI risks
AI risk is a people, data and accountability issue.
AI can improve productivity, service quality, analysis, decision support and innovation. It can also create new risks and intensify existing risks if used without clear controls.
Filter risk topics
Risks every workplace should understand.
Use the filters to explore common areas. Many risks overlap, so a single AI use may need more than one control.
Privacy and data governance
AI tools may collect, process, infer or expose personal information. Poor data rules can lead to sensitive information being misused, shared or retained in unintended ways.
Output quality
Generative AI can produce confident but wrong answers, including false summaries, flawed reasoning, invented references or inaccurate advice.
Bias and discrimination
AI systems can reproduce or amplify unfair patterns in training data, system design or deployment.
Explainability
Some AI outputs are difficult to explain, which matters when decisions affect employment, services, safety or regulated processes.
Workforce impact
AI can change roles, workflows, skills, job security, workload and psychological safety. Poorly managed change can damage trust and culture.
Shadow AI
Unapproved AI tools can expose confidentiality, privacy, intellectual property, accuracy and compliance risks.
Vendor and supply chain risk
External AI providers, cloud infrastructure and foundation models can create dependencies that still need oversight.
Agentic AI risk
AI agents can plan and act across multiple steps. Weak instructions, access rights or monitoring can allow unintended actions.
Accountability gaps
Risk increases when no person or team is clearly responsible for approval, monitoring, incidents and review.
Hidden monitoring
Surveillance or productivity scoring can affect trust, autonomy, stress and fairness if limits are unclear.
Unsafe or high-impact use
AI advice used in safety-critical, employment, legal, health or customer-impacting settings needs stronger review and controls.
Three ways harm can arise
Risk is not only caused by a faulty model.
- System failure
The AI performs poorly, behaves unpredictably, produces biased output, becomes unreliable or contains security weaknesses.
- Malicious or misleading use
AI is used for scams, fraud, misinformation, impersonation, cyber attacks or unfair targeting.
- Inappropriate or reckless use
AI is deployed without authority, testing, human review, consultation or regard for foreseeable harm.
Risk management principle
AI risk belongs in the normal risk framework.
AI should be integrated into privacy, cyber security, data governance, procurement, legal, workplace safety, audit, assurance and incident response processes.