Mark Mitchell,
With the federal government clearly embracing the potential benefits of generative AI and a skyrocketing number of deployments across agencies, we can expect more debate between those who want to jump into the AI pool quickly and with both feet to reap the benefits and those more concerned about the security and privacy implications and want to take a more cautious approach.
Both sides of the argument have their merits. Ultimately, agencies should proceed with their AI plans while taking steps to address the very real security concerns.
On one hand, it would appear the faction that wants to jump in quickly has the advantage. We recently saw the release of a White House AI Action Plan and some accompanying executive orders that may effectively lift the guardrails previously put in place by those advocating caution. Even more to the point, the Government Accountability Office recently reported a ninefold increase in generative AI use cases across federal agencies from 2023 to 2024, and the General Services Administration has awarded multiple deeply discounted contracts for generative AI solutions to OpenAI, Anthropic and others – indicating that this train has pretty much already left the station.
Still, legitimate security and privacy concerns abound. Many in government raised concerns about how these AI tools are capturing federal government data and where that data is going, both internally and externally. Other issues revolve around the cost of storing all of the data that platforms are generating on the back end and dealing with the information overload resulting from AI deployments.
It is clear that the federal leadership is betting big on AI. While security and privacy concerns are always relevant, it’s important to reiterate that security is a business-enabling service line that maintains compliance and drives down risk to the mission to an acceptable level.
To do this, agencies should consider four steps to address security and privacy concerns when deploying new AI use cases.
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