The AI Risk Landscape

Here are the eight core AI risks every GRC program needs to address.

1. Hallucinations

AI systems, particularly large language models, can generate outputs that sound plausible but are simply wrong. They "hallucinate" facts, citations, and conclusions. Air Canada's chatbot infamously promised a bereavement discount that didn't exist, and the company was held legally responsible. Managing this risk requires human oversight of AI outputs, especially in high-stakes contexts, plus validation processes and clearly defined use cases.

1. Data Privacy Risk

AI systems consume vast amounts of data, and GenAI systems can unintentionally surface sensitive information. Employees sharing confidential data with external AI tools — a common real-world occurrence — creates significant exposure. The fix involves a combination of anonymization techniques, access controls, secure development practices, and monitoring for data leaks.

1. Lack of Transparency and Explainability

Many advanced AI systems are "black boxes" — even their creators can't fully explain their decision-making. When a system denies a loan or flags a health concern, and no one can explain why, that's both an operational problem and a legal one. Organizations need documentation of every model in use, explanation methodologies, and continuous monitoring for bias and drift.

1. Adversarial Attacks

AI systems can be manipulated. Prompt injection attacks trick a model into following malicious instructions. Data poisoning corrupts training data to produce skewed results. Insecure AI-generated code creates vulnerabilities in software. AI worms are an emerging threat that use AI models to spread malware. Defense requires secure development lifecycles, least-privilege access, and red-team exercises.

1. Data Manipulation and Poisoning

Similar to adversarial attacks but worth naming separately: when someone intentionally corrupts the data used to train AI models, the results can include flawed predictions, embedded biases, and security vulnerabilities. Detecting this requires anomaly detection processes and secure data pipelines.

1. Bias

AI systems can amplify and perpetuate the biases in their training data. The consequences are not theoretical. MIT researchers found racial bias in risk prediction algorithms. Amazon had to scrap an AI hiring tool that systematically penalized resumes from women. Bias audits, diverse training data, and fairness metrics are all part of the mitigation toolkit.

1. Scalability and Malfunctions

AI models that perform well in controlled conditions can fail when scaled to the real world or confronted with situations outside their training distribution. In critical applications — healthcare, autonomous vehicles, financial systems — those failures can be severe. Solid SDLC practices, capacity planning, and governance all play a role.

1. Shadow AI

Employees using AI tools without oversight — summarizing meetings, writing emails, generating code — are the modern equivalent of Shadow IT. The tools may be harmless in intent, but they create blind spots: unknown data flows, unreviewed compliance risks, potential leaks of sensitive information. Monitoring, clear usage policies, and a formal intake process for AI tools are the response.

AI risks are not theoretical. The real-world examples are already piling up. The organizations that take these risks seriously now will be far better positioned than those who wait for a breach to prompt action.