Advancements in GenAI are opening up opportunities to accelerate innovation, automate toil, make learning faster and easier, and amplify the impact of every developer’s work. But AI can also introduce threats into your business landscape, from security issues to ethical concerns to AI’s macro impact on factors like diversity, equity, and inclusion (DEI).

Security risks

AI tools, like anything new you incorporate into your tech stack, become another potential attack surface that you have to protect. In other words, they introduce new security risks to be mitigated.

Protecting against AI security risks means securing reams of data that include highly sensitive personal information, like medical or financial details, from attacks that target AI and ML applications. It also means protecting your organization’s proprietary intellectual property (IP) from unauthorized dissemination or other potential misuses. For example, ML algorithms can be trained to reverse-engineer patented technologies, analyzing the functionality and output until they can infer and recreate the underlying processes—effectively stealing the IP.

Here are two major security and privacy risks that you should bear in mind when evaluating AI technologies:

Data breaches and invasive data collection

AI systems can collect and analyze vast amounts of data, some of which may be personal or sensitive. AI systems can also be exploited to gain unauthorized access to this data. For example, bad actors might use an AI to analyze social media content to infer personal information about people without their knowledge and consent. It’s both a security requirement and an ethical one for organizations to protect themselves against data breaches or unauthorized data collection enabled by AI.

Intellectual property (IP) concerns

Machine-learning algorithms can be trained to reverse-engineer patented technologies. By analyzing the output and functionality of a technology, an AI system can infer the underlying processes and recreate them, effectively stealing the IP. The question of who owns IP generated by an AI is a murky legal area. For instance, if a business uses an AI system to design a new product, who owns the rights to that product: the business or the person/company that developed the AI? How questions like this are answered across different jurisdictions could have a big impact on a business’s profitability and legal exposure going forward.

Ethics and AI

It’s critical for organizations to be aware of the ethics of AI: the moral principles that inform how we develop, test, and use AI in the world. Major organizations like Google and IBM have foregrounded the importance of ethical AI and signaled their commitment to upholding the three main principles (PDF) that guide responsible, trustworthy experiment and algorithm design:

• Respect for individuals
• Benefitting society as a whole
• Equitable distribution of benefits and burdens of AI

As you consider how you might use AI to help achieve your business goals and decide which tools to integrate into your workflows, make sure you’re working with AI companies that adhere to ethical AI standards. You also need to be cognizant of how your users might leverage new AI-powered tools in less-than-ethical ways.

AI impact on DEI

The relationship between AI and diversity, equity, and inclusion (DEI) in the workplace is another area that organizations that develop and use AI-powered technologies need to consider. AI can help reduce the effects of bias—but it can also reproduce and perpetuate bias. After all, AI models are trained on every bit of information we provide, including the biases and prejudices we carry with us as humans.

This is a major concern for advocates of ethical AI, especially when it comes to highly sensitive arenas like healthcare, banking, and criminal justice. Companies have a responsibility to ensure that the AI tools they use and/or develop are not regurgitating harmful biases, preventing people from accessing resources equitably, or reversing recent DEI gains.