Operationalizing AI Red Teaming: A Practical Framework for DoD Assurance

Red-teaming can’t stay a side exercise.

If AI is entering targeting, ISR, autonomy, and decision-support, then assurance has to rise to the same operational level.

Over the last few months, I’ve been studying how MIT Lincoln Laboratory is approaching AI red-teaming - not as a checklist, but as infrastructure. What stood out is how their work ties together adversarial testing, mission assurance, acquisition, and congressional oversight in a way that DoD programs can actually use.

Operationalizing AI Red Teaming: A Practical Framework for DoD Assurance

So I wrote this paper to map the full picture:
- how red-teaming fits into DoD’s Responsible AI and Assurance priorities
- how techniques like adversarial ML, data poisoning tests, and EW-stress autonomy translate into mission risk
- where MIT LL fits relative to SEI, MITRE, RAND, APL, Aerospace
- what a 12-month DoD playbook for operationalizing assurance could look like
- and how we turn red-teaming from “good practice” into repeatable, briefable evidence for fielding decisions.

If AI is going to be a battlespace, then red-teaming has to become the rehearsal - not an afterthought.

I’m sharing the full paper here because so many teams across DoD, industry, and academia are asking the same question:

How do we pressure-test AI systems with the same seriousness we test everything else we depend on?

Would love to hear how others are operationalizing this on their side of the mission.

Final Word: Red-teaming is no longer a safety check - it’s the battlespace rehearsal DoD should be running before adversaries run it for us.