4 Best Practices for Managing Generative AI Risk
How to manage generative AI risk against its benefits
The opportunities and benefits of harnessing GenAI for your business are undeniable but. However, when considering the use of generative AI for your business, AI risks—both known and unknown—need to be understood and assessed in order to be managed proactively. This article will help you learn how GRC (Governance, Risk and Compliance) software can manage GenAI risk.
Like any powerful new technology, GenAI is being used across departments and industries. Some functions, like marketing chatbots, have already deployed GenAI in ways we can see. But the reality is that a wide range of departments—from customer service to IT to engineering—are also experimenting with it. To deploy GenAI safely and grant access across your organization, GRC software operates as a critical tool in your toolbox. GRC tools are already made to monitor and manage potential risks, especially when it comes to GenAI risks associated with:
- cybersecurity
- privacy
- compliance
- customer and partner relationships
- ethical and legal obligations
GenAI opportunities and applications are virtually everywhere with tools like Open Source’s Chat GPT and Midjourney, which means there isn’t a “one size-fits-all” solution for managing AI risk. Each industry has its own unique set of requirements, and each specific use case has its own risks. (This is precisely when a reliable GRC software solution is clutch to scale and adapt to shifting business needs.)
How to manage GenAI risk in your GRC program
Regulatory compliance already plays a critical role for policies and controls related to cybersecurity, fair use, transparency, ethical adherence, data privacy, IP infringement, and others. Formalized guidance can come from risk management frameworks, too, like NIST AI RMF Core. Auditors also contribute significantly to the launch of GenAI risk management programs. That said, it’s up to risk managers to:
- develop and implement guidelines that provide greater transparency
- assess risk for a range of use cases
- ensure data privacy
- adhere to current security requirements
Let’s look at those four key areas of GenAI risk management and how organizations can address them to manage risk as part of a greater GRC management program.
1. Transparency
Most commercial Gen AI products are not transparent, meaning that many GenAI products aim to protect their AI algorithms as intellectual property. Thus, they often function as “black boxes,” making it difficult for customers to audit them. Users likely don’t know what foundational data was used to “train” the system or how it’s processed. The output from GenAI tools and AI-powered products needs to be judged for data integrity against standards for fairness, bias, and regulatory compliance.
Some organizations might use black box techniques to audit these AI systems. However, in black box audits, testing methodologies like equivalence partitioning, boundary value analysis, and other forms of functional testing are used to infer behaviors without knowing the underlying processes. In contrast, audits of transparent systems can use white box testing techniques, such as static code analysis and unit testing, which rely on access to the internal code and structures.
Essentially, a black box audit is more about evaluating what the system does rather than how it does it, which can limit the depth of a full audit.
2. Risk
We already know that there are external and internal risks associated with the rise of GenAI that need to be assessed and addressed. According to a PwC survey, executive respondents cite data breaches and cybersecurity attacks as the top two areas of AI concern. Threat actors are already using GenAI in phishing attacks to make them more effective and this will continue to evolve. This trend will likely accelerate the convergence of GRC with cybersecurity.
Because of this uptick in malicious actors, AI technologies are subject to increasing regulation. This means that organizations must ensure that their AI systems comply with relevant laws and industry standards to avoid legal penalties and reputational damage.
Reputational damage can also come as a result of using AI content that is prone to hallucinations and AI biases. AI systems can inadvertently perpetuate or amplify algorithmic biases or inaccuracies present in biased training data. There is still room for error with AI using predictive analytics models. That’s why it’s important to continuously monitor and test AI outputs for bias or unexpected behaviors and take steps to mitigate any unfair or false outcomes.
There are also ethical standards for AI use to consider. Whether intentional or inadvertent, issues such as invasion of privacy, intellectual property issues, copyright violations, IP infringement or other unethical outcomes are part and parcel with GenAI.
Internally, organizations need to control GenAI risks and ascertain trustworthy AI systems by using their GRC software to set up governance policies, assess risks, understand the data being used, and protect it.
>> Building Cybersecurity into Your Compliance Program: Watch now
3. Data Privacy
You’ve already experienced the power of personal digitized data if you’ve ever visited an website and suddenly see its advertising at every turn. GenAI promises to turbocharge this phenomenon. Generative AI systems often require large amounts of data from which to learn and regurgitate. That data can include sensitive or personal information that was never intended to be used in this way.
Additionally, advanced AI systems can use “scraping” AI algorithms to capture photos and other visual content and use it for unintended purposes. This adds fire to mounting privacy concerns. Organizations need to ensure that their GenAI is not using unauthorized access to personal data. Otherwise, they will run afoul of regulations and anyone impacted by its abuse—including employees, customers, and partners—particularly when it comes to GDPR or HIPAA. Many also look to Service Organization Control 2 (SOC 2) to establish robust information security practices against data theft. It’s another way to build customer credibility and public trust as you shield against adversarial attacks, unauthorized access and vulnerabilities.
Risk managers should initiate proactive measures now, if they haven’t already. Before deploying generative AI technologies, conduct data privacy impact assessments to identify and mitigate potential privacy risks associated with the processing of personal data. This assessment should consider the types of data the AI will process, how data is collected, stored, and used, and the potential impacts on individual privacy.
4. Data Security
The perennial “cat and mouse” game between threat actors and their targets promises to be more intense than ever with the advent of GenAI. Security risks have increased because there are a number of attack vectors that can be aided by the technology, including:
- the rapid generation of malware and automated attacks
- data poisoning
- model tampering
- data theft
Cybercriminals have taken advantage of this by creating their own large language models focused on fraud, such as WormGPT and FraudGPT for malicious use.
At the same time, cybersecurity organizations are embracing GenAI to become smarter and faster at identifying threat actors and preventing attacks. A multi-layered security approach is required to address these threats, including robust data handling practices, continuous monitoring of AI systems, and the implementation of advanced cybersecurity measures tailored to the specific vulnerabilities of generative AI technologies.
Effective GenAI risk management starts with strong governance
GenAI underlines the importance of the “G” in GRC. A clear mission helps to govern use of AI in your company while also allowing for a wide range of use cases, risks, and functions critical to success. Governance based upon responsible use will give your GenAI initiatives the most secure start, protecting partners, customers, and employees as the technology evolves.
GenAI is, indeed, an exciting new technology. But organizations must be ready for its many risks and potential dangers. Successfully managing the risks and ongoing regulatory compliance will enable GenAI to safely and securely transform for your business.
For more information on preparing your organization for GenAI, contact Onspring at hello@onspring.com.
