Understanding GenAI’s impact on risk management
By now, most of us have experienced both the power and unpredictability of generative AI. Systems like ChatGPT can produce original content, including text, images, code, and video, by learning patterns from information found online. Naturally, this technology has quickly made its way into the workplace. In some cases, GenAI was introduced thoughtfully with a specific purpose in mind, or through software updates that incorporated GenAI capabilities. As the practice of including GenAI in our workflows and in the software we use everyday increases, we must be aware of the inherent risks that GenAI introduces. Let’s explore the major risk categories, such as strategic, operational, technological, compliance, and reputational risks, that we must address before adopting GenAI into our workplace.
Strategic risk
While generative AI capability brings immense value, it also carries significant risk. Strategically, organizations may find themselves overly reliant on AI-generated outputs without fully understanding their limitations. Decisions influenced by flawed AI models or inaccurate outputs can misalign with long-term objectives, resulting in costly missteps. Further, the assumption that generative AI will automatically create efficiencies or new opportunities may result in overinvestment in tools that lack sufficient governance or business alignment.
Operational risk
Generative AI tools can introduce hidden vulnerabilities. One of the most pressing concerns is data leakage. Employees may unintentionally share confidential or proprietary information with publicly available AI tools that can retain and use that data to train future models. Additionally, these models are prone to what experts call “hallucinations,” where the AI generates content that appears plausible but is entirely incorrect. In highly regulated industries, such as those in the legal, financial, or medical sectors, this can lead to significant errors, cause harm to individuals, or result in compliance violations.
Technology risk
We also need to understand the risks associated with shadow AI. In some cases, generative AI is quietly introduced through existing software updates without the organization’s knowledge. In other instances, employees may purchase and use their personal GenAI accounts at work. In either case, the pace of deployment could bypass traditional software vetting and change management practices, meaning these tools are integrated into workflows without adequate oversight or testing.
Compliance risk
Governments and regulatory bodies are enacting frameworks governing AI. The European Union’s AI Act, U.S. executive orders, and emerging guidelines from agencies such as the FTC and SEC are placing new obligations on organizations to ensure transparency, accountability, and fairness in their use of AI. At least in part, this is because many generative models are trained on datasets that may include copyrighted material, personally identifiable information (PII), or biased content, raising concerns around intellectual property rights, data protection, and discriminatory outcomes. Organizations using third-party AI platforms must also contend with heightened third-party risk, especially when vendors fail to disclose their training data or model architecture.
Reputational risk
Perhaps the most difficult category of AI risk to quantify is reputational. A single misuse of generative AI can quickly escalate into a public relations crisis, particularly if it involves customer-facing content, intellectual property, or a breach of trust through the disclosure of confidential information. Trust is hard to regain once lost. Inappropriate, biased, or misleading content generated by AI can damage customer loyalty, investor confidence, and employee morale. Internally, poor communication about AI policies and controls can breed fear, confusion, or resentment among staff, especially if employees view AI as a threat to their roles.