The Pentagon is racing to integrate artificial intelligence into every corner of its operations, from logistics to intelligence to warfighting. But there’s a critical blind spot: most investments focus on building AI agents without securing them.
These autonomous systems that can act, learn, and connect across networks introduce risks that traditional AI protections weren’t designed to handle.
When decisions happen at machine speed and mistakes have real-world consequences, unchecked agents aren’t just a technical challenge but a battlefield liability.
The AI Security Landscape Has Shifted
Initially, AI security focused on chatbots and copilots. Interactions were straightforward: a prompt, then a response. Reviewing each exchange in isolation was usually sufficient.
Now, the equation has changed. AI agents operate with memory, autonomy, and access to tools and systems. Risk is no longer confined to a single interaction — it builds over time, across a chain of decisions. Traditional safeguards are far less effective in this environment.
Misalignment in AI agents is often gradual.
Consider a logistics AI agent supporting aircraft readiness: it has access to maintenance records, fuel inventories, and transportation data, and can generate plans and submit requests automatically.
While these capabilities support warfighters, they also create a double-edged sword.
Misaligned objectives, faulty assumptions, or unexpected system interactions can cause the agent to go “rogue,” overstepping authority or pursuing optimization goals too aggressively.
There is no single point-in-time breach. Instead, over weeks, misalignment accumulates, leading to misallocated resources and degraded mission outcomes.
This is what an AI agent compromise looks like: not just a data breach, but the slow erosion of operational success.
Intentions Matter, Even for AI
Addressing this risk requires redefining AI guardrails.
Historically, the Pentagon focused on protecting data. Today, the priority must shift to understanding and governing the agent’s behavior itself.
This approach can be broken into two complementary layers: intent alignment and behavioral evaluation.
Intent alignment verifies that the agent’s actions match the user’s stated objective.
Returning to the logistics example, if the agent requests information about combatants in an upcoming mission while trying to improve aircraft readiness, there is a misalignment between goal and action. Addressing this misalignment is the first layer of control.
Behavioral evaluation compares an agent’s actions against expected norms, based on past activity across users, agents, and applications.
Evaluating an agent across sessions ensures actions are reviewed in context. Historical patterns define expected intent, scope, and authority. Deviations from this baseline can trigger alerts and human review before there is operational impact.
Together, this two-pronged approach moves security from inspecting outputs to validating purpose, consistency, and trajectory.
Intent is Key to AI Dominance
AI agents are central to achieving the White House’s goal of “global AI dominance.” They offer enormous potential, enabling the Department of War to operate at machine speed.
But intent security must sit at the forefront of investment. With AI agents already being integrated into workflows from vendors such as OpenAI, the risk of misaligned or uncontrolled behavior is real and urgent.
Shifting to intent-focused governance ensures AI systems act predictably, safely, and aligned with mission objectives.
Without it, speed and autonomy stop being advantages and start becoming operational risks.
Elad Schulman is CEO and Co-Founder of Lasso Security.
The views and opinions expressed here are those of the author and do not necessarily reflect the editorial position of Military AI.
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