Zero Trust Passes Key Milestone

In January 2022, the Zero Trust Federal Strategy set a deadline of September 30, 2024, for agencies to adopt some level of zero trust architecture. Based on early indications, agencies have largely met zero-trust goals. The Federal CIO reported in early September that the 24 CFO Act agencies were all over 90% of the way to meeting the zero-trust goals. Beyond that group, the federal government as a whole was at 87% goal completion.

What's Changed?

The shift to zero trust is a response to the way government and citizens are using technology. With the increased use of cloud-based solutions, the traditional "castle and moat" security that protected on-premise infrastructure no longer supports the way applications are being deployed. Zero Trust focuses on continually verifying that users have permission to access the data and systems they are using. Gaining access requires coordination among a number of technologies that all work with a common set of user identification and access policies. Continue reading

Meet the Chief AI Officer

The executive order (EO) on artificial intelligence, issued in October of 2023, calls on agencies to designate a chief artificial intelligence officer (CAIO) responsible for coordinating AI use, promoting AI innovation, and handling AI-related risk management within their agency. Efforts are underway to codify this mandate, with bills introduced in the House and Senate that would turn the EO recommendation into law.

In the year since the mandate and in advance of legislated requirements, agencies have worked to not only fill but define this new CAIO role. In some instances, CAIO duties have been added to the job description for an existing executive--typically the chief data officer or chief technology officer--but in others, a stand-alone position has been created to meet the agency's AI needs. Continue reading

How Government is Acquiring AI

Just as cloud computing upended how government buys technology, agencies are now having to adapt to acquire fast-evolving artificial intelligence (AI) technology. AI is proving to be a key tool in helping government improve the efficiency and connection of its workforce and deliver improved service to citizens, but the promises of this new technology come with risks. To ensure AI solutions are secure and ethically designed, agencies are implementing a number of guardrails to ensure the safe and effective use of powerful technology.

How to Use AI

The Office of Management and Budget (OMB) developed a policy document to harness the benefits and mitigate the risks of AI for Federal agencies. This guidance provides details on how to use AI securely and effectively with a focus on five key areas: risk management, transparency, responsible innovation, workforce, and governance. Continue reading

AI’s Role in Higher Ed

Higher education is at an interesting inflection point. While there has been much talk about the increasing cost for students, educational institutions are not necessarily reaping the benefits of these higher prices. Colleges and universities are seeing enrollment numbers decrease due to affordability concerns and a general decline in population. Additionally, the rise of the gig economy and online degree options, as well as the willingness of employers to hire and train people without college degrees for in-demand jobs in cybersecurity and artificial intelligence (AI), are all impacting the perceived necessity of advanced education.

Show Me the Data

While AI careers may no longer require a four-year degree, AI technology is proving to be invaluable in supporting the growth and success of higher education institutions. With all of the outside pressures, colleges and universities need to take a serious look at all the data they hold to determine the cost and ROI of the programs they offer, from degree options to sports teams to housing options. Information about student demographics, student performance, program revenues, and operational costs are all held in siloed systems. AI can help collate all of these disparate data sets, making connections that would take teams of humans months or years to discover. Continue reading