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Intel DevOps AI

Machine Intelligence for the Intelligence Mission

Recent Writing

cyber attributionBayesian MLthreat intelligence

Attribution Under Ambiguity: Using Bayesian ML to Assign Cyber Operations to State Actors

How Bayesian machine learning models improve cyber attribution by quantifying uncertainty across competing hypotheses when evidence is fragmentary and adversaries actively deceive.

· 5 min read

Causal Inference in Intelligence Analysis: Moving ML Models from Correlation to Explanation

Human-Machine Teaming in Intelligence Analysis: Designing Workflows Where Analysts and AI Actually Work Together

Retrieval-Augmented Generation vs. Fine-Tuning for Intelligence Workflows: Choosing the Right Adaptation Strategy

Synthetic Data Generation for Intelligence ML: Building Training Sets When Real Data Is Classified or Nonexistent

Confidence Calibration in Intelligence ML Systems: Closing the Gap Between Predicted and Actual Accuracy
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Intel DevOps AI explores AI/ML for intelligence operations, autonomous OSINT, LLM-augmented analysis, AI in national security, predictive intelligence, machine learning pipelines for intel workflows. New writing published regularly.