With the economy downshifting, organizations are increasingly looking to artificial intelligence (AI) to help them do more with less. As this trend continues to expand, ensuring robust AI governance will be essential. Appropriate AI governance begins with an accurate inventory of all SaaS applications and AI tools in use across the organization. This means being prepared to address shadow AI and embedded features, too. Generative AI is here — its adoption has moved faster than anything we’ve ever seen. According to a recent EY survey, 95% of all U.S. companies are now leveraging these powerful tools, making it much more difficult to monitor and manage AI deployment.
The need for an AI use policy is urgent. From this, organizations need to establish strong expectations for data management practices and vetted tools. They need to require a solid vetting process for new technologies. Security teams now have to meet the new challenges of AI governance. So long as they overcome these challenges by striking a balance between innovation and security, everyone wins.
The Challenges of Visibility and Inventory Management
Even within the SaaS paradigm, visibility poses one of the biggest challenges to effective AI governance. And with data constantly in motion to third-party cloud services, their migratory nature further frustrates oversight attempts. First, organizations need to take an active inventory of all applications and tools they’re deploying. This framework extends to recognizing the ones that you don’t see operating behind the scenes, sometimes called shadow AI.
Tracking, monitoring, and managing these tools across hundreds of SaaS applications requires an extraordinary amount of manual labor. This increased workload can become a burden to security teams in no time. This is a dangerous state of affairs as the most important questions of accountability and oversight hang in the balance. Without a single point of governance, there is plenty of opportunity for relevant topics to get missed entirely.
“You can’t secure what you don’t even realize is there.” – Anonymous
To address these complications, experts recommend requiring organizations to use automated monitoring tools to track how AI is accessing data. These tools can add active oversight and alerting, ensuring security teams are always aware of any new vulnerabilities that arise in their quickly evolving AI landscape.
Establishing Clear Policies and Access Controls
Developing a robust policy around responsible AI usage is more than compliance. It promotes the agency-wide culture where employees can experiment with new ideas, but security is still job number one. A strong policy should succinctly be transparent about data collection, use, and sharing. It needs to outline what tools are approved for use and a vetting process for new technology.
Access control measures contribute to this false framework. Principles like least privilege access, where people only have access to what they need in their job, help to keep information secure. Frequent permission reviews are just as essential in ensuring that we feel this much control. Nonprofits need to implement and continuously update risk assessments often. This allows them to test out new tools, vendor updates, and emerging threats, fortifying their governance strategy.
“Bring the same rigor to AI that’s applied to other technology – without stifling innovation.” – Anonymous
Maintaining this fragile balance requires unwavering vigilance and devotion from leadership. Thus, organizations need to create mechanisms for periodically re-assessing risks. Establishing a framework for monthly or quarterly reviews would go a long way towards ensuring the AI governance framework remains relevant as technology and risks evolve.
The Evolving Landscape of Generative AI
The speed at which generative AI has been incorporated into legacy software applications is exciting but daunting for organizations. Although these tools stand the potential to greatly increase productivity among public-sector workers, they pose considerable risks to privacy and security. According to a survey, over 27% of organizations have outright banned generative AI tools following privacy scares, illustrating the tension between innovation and risk management.
Today, vendors are scrambling to put GPT AI copilots and assistants in their SaaS applications. This boom is a testament to the rising demand for smart, innovative solutions. With this opportunity comes the responsibility of ensuring that organizations don’t let their guard down when it comes to good governance. Ongoing scrutiny and recalibration of policy will be necessary as new tools are developed and existing ones are refined.