Context-retaining AI tools are no longer theoretical – they have already taken root within federal agencies, offering an operational edge and allowing civil servants to devote more time to mission-critical responsibilities. Yet, the same capabilities that make them so effective—long-term data storage and adaptive learning—inevitably spark questions about compliance, security, and privacy. Navigating the tension between optimized workflows and the obligations inherent in government operations has become a defining challenge of AI’s next phase in the public sector.

In this article, we’ll highlight real-world lessons from organizations including the U.S. Air Force and U.S. Government Accountability Office (GAO), demonstrating both the potential benefits and pitfalls. Ultimately, a single Chief AI Officer cannot manage these challenges alone; a broader governance framework including cross-functional committees, structured policies, stringent safety and security controls, and mandatory user training, like NIST’s 2024 AI Risk Management Framework, is essential for the responsible implementation of AI in federal environments.

The Promise of Persistent AI Memory

Contextual AI can transform day-to-day government operations by eliminating repetitive data entry, curating and distilling large volumes of information, reducing follow-up questions, and accelerating research. This kind of personalized support is incredibly enticing, especially as agencies are stretched thin by high workloads and cuts to funding, where any automation that saves minutes per task can add up to substantial productivity gains. Yet, precisely because these tools “remember” so many details, they blur the line between ephemeral chat and official agency records. The knowledge they accumulate can include personally identifiable information (PII) or even mission-critical data that must be protected under strict federal standards.

Navigating the Compliance Puzzle

Federal records law is particularly stringent when it comes to storing and retrieving information related to agency activities. Any conversation that an AI system retains could, under certain circumstances, become a federal record subject to retention schedules and potential Freedom of Information Act (FOIA) requests. The Privacy Act of 1974 imposes additional constraints on how an agency processes personal data, and employees must consider how the AI might inadvertently log or recall sensitive conversations over time^[1]. 

These constraints intensify the importance of clarity and policy. An agency’s policy guidance should detail how users may feed data into the AI, how long the system can preserve specific conversation logs, and which records require official archiving. Otherwise, contextual AI runs the risk of quietly accumulating the kind of data that should be destroyed, redacted, or protected under more controlled conditions.

Securing Closed Environments

‍Many agencies opt to deploy their AI in closed, on-premises systems or within specialized, secure, and even classified cloud environments. This strategy limits data exposure to a defined internal network rather than an open internet model. A secure instance of a large language model trained strictly on agency-approved documents, for example, reduces the chance of private, classified, or mission-specific information leaking into a public AI service. 

Still, restricting these systems to a closed environment does not, by itself, solve every problem. If multiple departments feed a shared AI instance and user access levels are not carefully set, the AI may combine information from classified or higher-clearance projects with unclassified data, potentially enabling accidental “spillage” of classified information or cross-pollination of sensitive content. Agencies must, therefore, implement robust role-based access and encryption at every level, ensuring that only those with the proper credentials can unlock certain portions of the AI’s accumulated memory.

Addressing Novel Security Threats

Contextual AI brings novel challenges that go beyond standard cybersecurity concerns. Although agencies already grapple with unauthorized access, data exfiltration, and insider threats, AI-specific hazards such as “prompt injection”, “jailbreaking”, and “hallucinations” require heightened vigilance. 

With prompt injection, a malicious user might embed hidden instructions in their text, prompting the AI to reveal sensitive data to other users or store an override that remains active in the system’s memory. Jailbreaking refers to the act of bypassing built-in safeguards and policy constraints to make the Al model generate unauthorized, restricted, or adversarial outputs that it was explicitly designed to prevent. Hallucinations, meanwhile, occur when an AI draws incorrect or outdated conclusions and presents them with unwarranted confidence, risking misdirection on critical policy or regulatory tasks. 

In any of these scenarios, an agency’s reliance on the AI’s persistent memory could amplify any resulting damage. This makes security testing, red-teaming, and ongoing monitoring vital—an AI system needs continuous scrutiny to ensure it remains a trustworthy asset rather than a liability.

Designing Governance Beyond a Title

In October 2023, President Biden issued Executive Order 14110 on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, mandating that all federal agencies appoint a Chief AI Officer (CAIO) to oversee AI-related policies, governance, and risk management^[2]. Most agencies now designate a CAIO to steer AI strategy, but complex systems that aggregate potentially sensitive data demand more than a single figurehead. A cross-functional governance committee with the CIO, CISO, legal counsel, privacy and ethics officers, and mission leaders is better suited to monitor and approve AI use cases agency-wide. The committee can require privacy and security assessments for each AI pilot, gauge the potential ramifications of storing a certain category of data in the system, and ensure all relevant stakeholders—especially records management teams—have reviewed potential archival requirements. 

Because contextual AI inherently overlaps legal, technical, and operational domains, this broader lens is critical. Carefully structured governance ensures that pilots start small, remain contained until they’re validated, and are scaled in a controlled manner once security and privacy measures are demonstrably effective.

Real-World Lessons: Air Force and GAO

Some agencies are already testing these tools in controlled pilots. The Air Force, for instance, has experimented with a secure generative AI platform called NIPRGPT that maintains strict data isolation to ensure that no single user’s queries bleed into another’s session^[3]. This small-scale rollout has helped pinpoint technical vulnerabilities—such as potential data leakage and prompt injection—early on. Meanwhile, the Government Accountability Office has implemented its own internally developed chatbot, dubbed “Project Galileo,” which draws on a carefully curated repository of GAO documents^{[4] [5]}. By restricting the AI’s “creativity” to prevent misinformation, GAO reduces the risk of hallucinations while still benefiting from speedier data retrieval. Both examples illustrate the balancing act: these agencies reap efficiency gains but only after instituting tight controls, rigorous red-teaming, and phased deployment strategies that address security, privacy, and compliance from the outset.

Shaping Employee Culture and Training

Governance policies are only as effective as the employees who put them into practice. As federal agencies increasingly adopt AI tools capable of learning and adapting based on user inputs, comprehensive employee training becomes critical. Every user must clearly understand the fundamentals of the AI platforms they engage with, recognize the limits of safe data disclosure, and possess the analytical skills necessary to critically evaluate AI-generated information.

Mandatory training for all federal employees should be standard practice. Regular workshops, interactive training modules, and periodic refresher courses can reinforce best practices, emphasizing the critical importance of never entering sensitive or classified information unless explicitly authorized for that security level. Training should also equip staff with practical techniques to validate AI outputs, given the potential for context-rich systems to produce seemingly credible yet occasionally incorrect responses. Fostering a culture grounded in healthy skepticism and continuous vigilance will help prevent inadvertent disclosures and maintain data security.

Looking Toward the Future

As agencies grow more proficient with memory-enabled AI platforms, new avenues will open for enhancing efficiency in document creation, optimizing workflows, and facilitating seamless inter-agency collaboration. However, personalized, context-driven AI demands elevated governance standards. Robust data governance frameworks, combined with stringent operational oversight, ensure AI tools remain aligned with an agency’s legal mandates and operational imperatives.

Moreover, agencies should closely examine and integrate insights from industry-established Responsible AI Frameworks developed by leading frontier AI organizations, such as OpenAI and Anthropic. Understanding these frameworks—whether developing AI solutions internally or outsourcing development—enables agencies to proactively identify risks, strengthen compliance, and uphold stringent federal security standards.

For a structured assessment of these frameworks, agencies can leverage resources like METR (Model Evaluation and Threat Research), which curates and publishes a centralized repository of Responsible AI Frameworks. By consulting METR’s database, federal entities can benchmark their governance structures and risk mitigation strategies against industry best practices.

Ultimately, securing AI platforms within controlled environments, consistently stress-testing their resilience, establishing comprehensive oversight mechanisms, and rigorously training the workforce are critical steps for success. When thoughtfully implemented and managed, contextual AI can empower federal employees to dedicate their expertise to strategic decision-making, leadership, and mission-critical responsibilities—positioning agencies to confidently navigate the challenges and opportunities of tomorrow’s digital landscape.

Sources‍

1 U.S. Department of Justice, Office of Privacy and Civil Liberties. (n.d.). Privacy Act of 1974. Retrieved March 11, 2025, from https://www.justice.gov/opcl/privacy-act-1974

2 Office of Management and Budget. (2024, March). M-24-10: Advancing governance, innovation, and risk management for agency use of artificial intelligence (Memorandum No. M-24-10). The White House. https://www.whitehouse.gov/wp-content/uploads/2024/03/M-24-10-Advancing-Governance-Innovation-and-Risk-Management-for-Agency-Use-of-Artificial-Intelligence.pdf

3 AFCEA SIGNAL Media. (2024, June 12). Air Force enables responsible experimenting with generative AI. AFCEA SIGNAL. Retrieved March 11, 2025, from https://www.afcea.org/signal-media/defense-operations/air-force-enables-responsible-experimenting-generative-ai

4 Ariga, T. (2024, August). Federal agencies experiment with generative AI while incorporating safeguards. FedTech Magazine. Retrieved March 11, 2025, from https://fedtechmagazine.com/article/2024/08/federal-agencies-experiment-generative-ai-while-incorporating-safeguards

5 U.S. Government Accountability Office. (2024, January). GAO’s work to leverage technology and ensure responsible use (GAO-24-107237) [Congressional testimony]. Retrieved March 11, 2025, from https://www.gao.gov/assets/d24107237.pdf