Reduce blindspots by conducting an AI risk analysis

- AI's potential to transform your business is vast, but make no mistake, it introduces new risks that demand diligent governance. Don't become another cautionary headline. Proactively prepare your organization to mitigate AI risk. In this video, I'll explain why any company that is serious about leveraging AI technology needs to prioritize doing an AI risk analysis. Any promise in technology comes with risks if not thoughtfully governed. This is especially true of AI, given the capabilities and access that the technology increasingly has. Here's why conducting a risk analysis is essential. One, it helps identify potential issues before they escalate, allowing for preventative measures rather than reactive ones. Two, it ensures that you meet legal and regulatory requirements and reduces the risk of penalties or legal challenges. Three, it'll help to identify and mitigate biases or ethical concerns. This is crucial to maintain trust with your consumers and upholding your corporate responsibility. And four, it will ultimately help identify operational challenges and contribute to smoother integration and deployment of your AI technologies. So how do you go about actually conducting an AI risk analysis? First, assemble a team, ideally consisting of legal, risk management, and technical experts. Then, start creating a detailed catalog of AI risk specific to your business. It's important to involve various stakeholders, including employees, customers, and external experts to get diverse perspectives on potential risks. I recommend that you focus on four key areas, one, data security, privacy, and permissions, two, model safety issues, such as hallucination and harmful content, three, ethical issues around bias, transparency, and intellectual property, and four, legal and regulatory compliance. For each area, catalog existing controls and rank the likelihood and impact levels of incidents if they occur, then detail specific steps engineering, product, and legal teams must take to strengthen policies, monitoring, and responses. You can leverage established frameworks like the National Institute of Standards and Technologies, AI Risk Management Framework, or Deloitte's Trustworthy AI Framework to guide your process. These frameworks offer structured methodologies for identifying and manage risk tailored to an organization's unique needs. Ultimately, recognize that continuous monitoring and adaptability will be needed. With AI technologies rapidly evolving and regulatory landscapes shifting, it's crucial to regularly update your risk analysis to stay ahead of new challenges. Now that you have watched this video, I encourage you to take some time to review the NIST's AI Risk Management Framework, and then proceed to create a robust AI risk management plan. Remember, the goal is to proactively manage AI risk, ensuring your AI initiatives are both innovative and safe. By doing so, you'll be setting your organization on a path to not just mitigate risk, but also to leverage AI responsibly and effectively. Prioritize mitigation planning can help prevent AI failures or scandals before they emerge. Don't wait for an incident to happen before you begin to act.