AI risk and AI in risk sound similar, but they point in opposite directions. AI risk is about the dangers created by AI itself; AI in risk is about using AI as a tool to improve risk management.
What is “AI risk”?
AI risk refers to the potential negative consequences that arise from developing, deploying and relying on artificial intelligence systems. It focuses on how AI can cause harm to people, organisations or society.
Key dimensions of AI risks include:
- AI Safety and reliability
AI systems can behave unpredictably, fail in edge cases or make errors at scale. A mis-classified loan application is one thing; an automated system mis-triaging thousands of medical cases or mis-routing power flows is quite another. - Bias and discrimination
If AI is trained on biased data, it can produce unfair outcomes: discriminatory hiring, skewed credit scoring, over- or under-policing specific communities, or unequal access to services. - Privacy and surveillance
AI enables large-scale analysis of personal data, facial recognition, behavioural tracking and profiling. This creates risks of privacy violations, intrusive surveillance and misuse of sensitive information. - Security and misuse
AI can be used to generate deepfakes, automate cyberattacks, write malicious code, optimize phishing campaigns or assist fraud. Attackers can also target AI systems themselves (e.g., data poisoning, adversarial attacks). - Over-reliance and loss of control
When organisations over-trust AI recommendations, they may deskill humans, weaken oversight and create single points of failure. In the extreme, there are long-term fears about highly autonomous systems acting misaligned with human goals. - Systemic and societal risks
At a macro level, AI can amplify misinformation, polarisation, labour displacement and inequality. Poorly governed AI in critical infrastructure, finance or defence can introduce new systemic fragilities.
AI risk, therefore, is not about “AI going wrong in the abstract” but about very concrete harms: wrong decisions, unfair treatment, loss of privacy, security breaches, safety incidents and erosion of trust.
What is “AI in risk”?
AI in risk (or AI for risk management) is about using AI systems as tools for risk identification, assessment, monitoring, and mitigation to manage risks more effectively. Instead of AI being the source of risk, it becomes part of the control environment.
Examples of AI in risk include:
- Fraud detection and financial crime
Machine learning models flag unusual transactions, behaviour patterns or counterparties that suggest fraud, money laundering or sanctions violations, often in real time. - Credit and market risk analytics
AI analyses large, complex datasets to detect early signals of credit deterioration, market stress or liquidity risk, supporting more informed capital and hedging decisions. - Cybersecurity and threat detection
In the context of AI for cybersecurity, AI-powered tools sift through logs, network traffic and endpoint data to spot anomalies, intrusions and emerging attack patterns faster than human analysts alone could manage. - Operational risk and supply chain risk
AI models forecast disruption risks (e.g., delays, bottlenecks, failures) by analysing IoT data, logistics feeds, weather, news and social signals, enabling earlier intervention. - Regulatory and compliance risk
Natural language processing helps scan regulations, contracts, emails and reports to detect compliance gaps, conflicts, insider-trading signals or conduct-risk red flags. - Enterprise risk dashboards and scenario analysis
AI helps aggregate and visualise risk data, simulate scenarios, and generate narrative explanations of risk exposures for boards and executives.
In all these cases, AI is a means to a familiar end: better visibility, faster detection, sharper forecasting and more targeted controls across the risk lifecycle.
Stark differences: direction of risk, object vs instrument
The core differences between AI risk and AI in risk can be summarised along a few stark contrasts:
- Object vs instrument
With AI risk, AI itself is the object of concern: “What risks does this AI system create?” With AI in risk, AI is an instrument: “How can this AI help us manage other risks?” - Threat vs control
AI risk treats AI as a potential threat vector that must be governed and constrained. AI in risk treats AI as a control or capability that strengthens your risk framework. - New risk vs better management of old risk
AI risk is largely about new or amplified risks: deepfakes, algorithmic bias, autonomous decisions at scale. AI in risk is mostly about managing existing risks (credit, fraud, operational, cyber) more effectively. - Governance focus
Managing AI risk focuses on AI ethics, safety, transparency, accountability, model governance and regulation. Using AI in risk focuses on integration into risk processes, data quality, performance and human-in-the-loop design.
If you conflate the two, you either underestimate the dangers of AI or you miss out on its value for enterprise risk management. The key is to hold both ideas in mind at once.
How to manage AI risk
To manage AI risk, organisations need a dedicated AI risk management framework and governance framework that typically includes:
- Risk-based classification of AI systems
Categorise AI use cases by potential impact (e.g., low, medium, high, critical). Hiring, credit, healthcare, critical infrastructure and law enforcement uses usually sit at the higher end. - Ethical and legal guardrails
Align with principles like fairness, explainability, accountability, privacy, safety and human oversight. Ensure AI compliance by aligning AI systems with emerging AI regulations and sector-specific rules. - Model governance and lifecycle controls
Apply robust processes around data selection and quality, model design, training, AI data security, validation, testing (including bias and robustness tests), deployment, monitoring, and retirement. - Human-in-the-loop and escalation
Keep humans involved in high-impact decisions. Define when humans can override AI, how they are trained to interpret AI outputs, and how issues are escalated. - Incident reporting and learning
Treat AI failures and near misses like safety incidents: investigate root causes, share lessons, update models and controls, and adjust policies. - Transparency and documentation
Maintain clear documentation of purpose, design choices, limitations and known risks for each AI system, so that auditors, regulators, customers and internal stakeholders can understand and challenge it.
Done well, AI risk management becomes part of your broader risk and governance ecosystem, not an afterthought bolted onto AI projects.
How to leverage AI in risk (safely)
When using AI to improve risk management, the goal is to harness its strengths while acknowledging its limitations:
- Start where data is rich and stakes are understood
Fraud, market anomalies, cyber threats and operational patterns are good starting points because they already have data streams and established risk metrics. This makes fraud risk management particularly well-suited for early AI deployment - Blend AI with traditional methods
Use AI models to augment, not replace, established quantitative and qualitative techniques. For example, combine AI-based early warning signals with expert judgement in credit committees or risk committees. - Design for interpretability where it matters
In regulated or high-impact domains, favour models and tools that can be explained, or wrap complex models with explanation layers that help humans understand key drivers. - Integrate with existing risk workflows
AI alerts that do not feed into clear processes (who reviews, who decides, what is escalated) will be ignored. Embed AI outputs into familiar workflows, dashboards and decision cycles. - Monitor for model drift and degradation
Risks and data patterns change. Continuously monitor model performance, recalibrate as needed, and formalise triggers for review. - Train people as much as models
Risk teams need to understand what AI can and cannot do, how to question its outputs, and how to use it responsibly. Upskilling risk professionals is as important as training the algorithms.
This way, AI becomes a powerful ally in risk management – but one that operates under clear guardrails, not as an infallible oracle.
Bringing it together: “two sides of the same coin”
The future of risk management will be shaped by both sides of this coin:
- Ignore AI risk, and you risk deploying powerful tools that create new vulnerabilities, harms and regulatory exposure.
- Ignore AI in risk, and you may find your organisation outpaced by competitors who use AI to see threats earlier, respond faster and make better risk-adjusted decisions.
The mature stance is to do both: build a thoughtful framework for AI risk, and, within that framework, deliberately deploy AI in risk. That means asking two questions for every AI initiative:
- “What risks does this AI create, and how will we control them?”
- “How can this AI help us see and manage other risks better than we do today?”
Answering both clearly is what separates organisations that are simply exposed to AI from those that demonstrate risk intelligence in an AI-driven world.
