AI Moves Fast. Trust Has to Come First.

The challenge was urgent and the margin for error was thin. A customer’s program needed a new approach, and Lockheed Martin’s AI Center (LAIC) was asked to help move it forward.

They did. And it wasn’t the first time.

The difference was a more disciplined approach to trust, testing and deployment. That discipline is what drives the work of the LAIC.
 

A Different Kind of AI Problem

Most organizations using AI today are consumers of it. Lockheed Martin is a producer. And it produces military-grade AI capabilities. That distinction carries a different set of responsibilities.

The LAIC's mandate is to do the hard work once and share it across the enterprise. Mike Harasimowicz, Director of AI Certifications and Standards, calls it "build once, use many." An AI optimization developed for sea-state conditions can be adapted for land and air, applied across guided missile cruisers, advanced fighters and ground-based missile defense systems. One investment. Many platforms.  
 

Mike Harasimowicz and team members at the LAIC.

Without that centralization, redundancy can proliferate and budgets can be wasted. The LAIC maintains an AI inventory that gives visibility into what is being built and what already exists, ensuring every new capability complements rather than duplicates what came before.

Building faster isn’t enough. In national security, AI has to be built tougher, too. It must be trusted under pressure, and that means testing it against the worst case before the worst case arrives.
 

AI Red Teaming

The concept of red teaming originates in warfighting. Red Flag, the Air Force exercise built around flying against the most capable adversarial forces available, was designed around a simple premise: you do not find out what works by operating in ideal conditions. You find out by facing the hardest version of the problem.

Red teaming turns that premise into a systems test. A dedicated team takes the role of the adversary, probing an organization's own plans and systems for weaknesses before a real opponent can find them.

That same approach works in cyber operations and in AI. Adversaries will attempt to confuse AI systems, introduce friction, inject deception and disrupt the decision cycle that effective operations depend on. Lockheed Martin brings nearly three decades of network-centric red teaming to that challenge. The cyber kill chain, one of the foundational frameworks in cybersecurity, was a well-renowned Lockheed Martin product. That institutional knowledge directly informs how the LAIC approaches AI security.

Mike says trust that has not been challenged is not trust. It is hope, and as the adage says, hope isn’t a strategy. That means you need a process rigorous enough to challenge it.
 

The RAGE Framework

That process has a name: RAGE. Responsible AI Governance and Evaluation.

RAGE is operationalized across Lockheed Martin's business areas. "People hear RAGE and think it sounds too aggressive," Mike says. "Good. If we don't aggressively pursue risk, risk will devour us."

The framework runs on multiple tollgates.

• The first asks why: Is there a sound business and ethical case before anything is built?
• The second asks how: Is the architecture right, is computing capacity sufficient, are user experience and interface accounted for?
• The third asks how much: Is it strong enough?
• The final ask is whether we go to production, and if we do, how will we monitor system performance?

"Before anything goes to production, I know why I'm building it, I know how I'm building it, and I know it can withstand contact with an adversary."
 

Pressure Testing

Trust must be engineered into the process from the start, which means understanding precisely what can go wrong.

Models drift. Data changes. Adversaries adapt. A system that is not regularly reviewed is a system quietly becoming a vulnerability. "An airplane comes in for maintenance," Mike says. "Your AI model does too. If you deploy it and forget it, it can quickly become a liability."

External risks are the most obvious, but there are internal threats, too.

For example, automation bias can occur when operators stop questioning outputs and blindly follow the system's recommendation. Data poisoning adds a deliberate dimension: adversaries corrupt training data to degrade performance or introduce exploitable behaviors. None of this is theoretical. It’s happening. “We need the best version of humanity on this team,” Mike says. “Critical thinking, creativity and intuition are necessary.”

Resilience also means recognizing that the largest AI models are not always the best solution. In some applications, smaller, purpose-built capabilities can provide greater security, efficiency and mission effectiveness, and the LAIC has invested in technologies designed to support that approach.

Through all of it, one principle holds: humans must be accountable. People are held responsible for decisions made, even when AI-enabled systems are involved. That requirement shapes how mission-ready AI is designed from the start.
 

Setting the Standard

Understanding failure is key to building resilience. And it’s what gives Lockheed Martin standing to do something beyond protecting its own systems.

"We're not theorizing," Mike says. "We're not creating hypotheticals. We're doing it."

That experience producing AI, fielding it and learning from it under real conditions carries a responsibility to help set the standard at the company, national and international levels. "We have a responsibility to help everybody do that so our national security is enhanced. A rising tide lifts all ships."

At Lockheed Martin, AI advantage and AI governance are mutually reinforcing.

Mike sets that as the mission for his team: drive innovative solutions by converging the need for governance with the pursuit of AI advantage.

"Those are not at odds with each other," he says. "They have to work in concert." The work is technical. It’s also human.