AI-Assisted OpSec Self-Assessment Handbook

In today's hyper‑connected environment, the very act of publishing a research note, commenting on a forum, or sharing a diagram can leave a trail that sophisticated adversaries are eager to follow. Modern threat actors now wield powerful AI systems that can ingest billions of public data points, stitch together disparate signals, and reconstruct a surprisingly detailed portrait of the analyst behind the screen.

This handbook is designed to give you a proactive, hands‑on framework for measuring and ultimately shrinking that portrait before an opponent has a chance to draw it. By treating AI as both a threat and a tool, you'll learn how to:

• Expose hidden fingerprints in your writing style, posting cadence, and media assets.
• Stress‑test your pseudonymous ("sock‑puppet") accounts against the same automated linkage techniques an adversary would employ.
• Map the indirect clues embedded in network behavior, device characteristics, and cross‑platform activity that can betray your true identity or location.
• The goal isn't to achieve perfect invisibility, an impossible target in a world where data is constantly harvested, but to raise the cost and uncertainty for anyone attempting to deanonymize you. By systematically auditing your public footprint with AI‑driven analyses, you gain the same insight that a hostile actor would have, allowing you to adjust habits, diversify patterns, and compartmentalize resources in a disciplined, evidence‑based way.

Through concise self‑assessment steps, concrete example prompts, and clear confidence metrics, this guide equips analysts, researchers, and security professionals with a practical playbook for staying one step ahead of AI‑enabled attribution. Use it as a living document: revisit the assessments regularly, iterate on mitigations, and treat each new public interaction as an opportunity to tighten your operational security.