AI Governance Frameworks in Defense

by Brandon Smith
February 17, 2025

Discover how AI governance frameworks build trust and resilience in dynamic, interactive, and customizable AI systems, addressing risks in real time.

Artificial intelligence (AI) is revolutionizing defense operations by enabling advanced data analysis, operational efficiency, and informed decision-making in complex and high-stakes environments. However, the defense sector faces unique challenges in trust, ethics, and security. Last year, 97 percent of surveyed organizations experienced at least one security breach related to generative AI.

The dynamic and customizable nature of AI systems in defense amplifies the need for robust AI governance frameworks. These frameworks must not only mitigate real-time risks but also ensure operational readiness and safeguard highly sensitive data and mission-critical operations. Implementing AI governance frameworks is essential to building trust, achieving resilience, and maintaining the integrity of defense AI systems in an increasingly unpredictable global landscape.

Why AI Governance Frameworks Matter

Effective AI governance frameworks allow an organization to leverage the strengths of AI in managing complex datasets and performing critical analyses, all while ensuring ethical and mission-aligned outputs.

The Memorandum on Advancing the United States’ Leadership in Artificial Intelligence emphasizes fostering safe, secure, and trustworthy AI development, and supporting global AI governance with clear risk management practices.

“The United States Government must continue cultivating a stable and responsible framework to advance international AI governance that fosters safe, secure, and trustworthy AI development and use; manages AI risks; realizes democratic values; respects human rights, civil rights, civil liberties, and privacy; and promotes worldwide benefits from AI,” states the memorandum.

This AI framework, referenced in the memorandum as the Framework to Advance AI Governance and Risk Management in National Security focuses on pillars to identify prohibited and “high-impact” AI use cases based on risk and the creation of robust minimum-risk management practices for those categories of AI that are identified as high impact. This is key for organizations tasked with the development of frontier AI.

Guiding Principles for Trustworthy AI in Defense

In the United States, the Department of Defense (DOD) has formally adopted five principles crucial for trustwothy AI.

Responsible – Appropriate levels of judgment and care will be exercised by DOD personnel while remaining responsible for the ongoing development, deployment, and use of AI capabilities.
Equitable – The department will take deliberate steps to minimize unintended bias in AI capabilities.
Traceable – The department’s AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology, development processes, and operational methods applicable to AI capabilities, including with transparent and auditable methodologies, data sources and design procedures and documentation.
Reliable – The department’s AI capabilities will have explicit, well-defined uses, and the safety, security, and effectiveness of such capabilities will be subject to testing and assurance within those defined uses across their entire life cycles.
Governable – The department will design and engineer AI capabilities to fulfill their intended functions while possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior.

In a 2022 publication entitled Ensuring Reliable AI in Real World Situations, Deloitte posits three distinct criteria for an AI system to meet before it may be considered ready to deploy in a real-world environment:

Reliability: The prediction accuracy meets expectations consistently over time, avoiding too many oversights or false alarms.
Stability: The model performs well both generally and under stress conditions, such as in edge cases. It is not overly sensitive to naturally occurring noise, targeted noise is covered by resilience.
Resilience: Model behavior is not easily manipulated through exploitation of vulnerabilities (either within the code or the training data).

Further, in the Deloitte Trustworthy AI framework, the organization offers a “multidimensional AI framework to help organizations develop ethical safeguards across seven key dimensions—a crucial step in managing the risks and capitalizing on the returns associated with artificial intelligence.”

Operational Challenges in Defense AI

The deployment of AI in defense operations comes with a unique set of challenges that can significantly impact the effectiveness of MLOps pipelines. Addressing these challenges is critical to ensuring AI systems deliver reliable, scalable, and mission-aligned outcomes.

Key challenges include:

Data Integrity and Poisoning Risks – AI systems depend on high-quality, unbiased data. However, identifying and removing “poisonous” or corrupted data from massive datasets remains a significant challenge. Such data can compromise the reliability of AI outputs, leading to errors in mission-critical scenarios. Proactive data governance and robust validation protocols are essential to mitigate these risks.
Overtraining and Model Generalization – Overtraining AI models on limited or overly specific datasets can lead to models that excel in narrow tasks but fail in dynamic or unpredictable environments. Ensuring models are robust and generalizable across diverse defense scenarios is key to maintaining operational effectiveness.
Pipeline Inefficiencies and Waste – Inefficiencies in the MLOps pipeline, such as redundant processes, prolonged iteration cycles, or ineffective model deployment strategies, can waste resources and delay critical innovations. Streamlined workflows and automation can significantly reduce bottlenecks, improve iteration speed, and enhance overall pipeline efficiency.
Scaling and Resource Constraints – Deploying AI solutions at scale often requires substantial computational resources, infrastructure, and skilled personnel. Balancing these demands with budget constraints while maintaining high operational readiness is a persistent challenge for defense organizations.

As AI governance frameworks must account for a high level of operational readiness to meet the changing needs of global defense, addressing visibility and AI explainability concerns are the first step in the journey to being able to effectively advance and evolve AI defense capabilities.

To learn more about AI Governance for Defense Applications, access the full white paper which reveals top challenges, key strategies within existing AI security frameworks, and phases of successful deployment.

If you are interested in accelerating iteration and repair cycle times for foundation AI models as part of your AI risk management strategy, contact us today.