Day 2 — Deepfake BEC, Vishing, and Synthetic Identity

Course: SEC5xx — Detecting and Responding to AI-Generated Adversary Content Day: 2 of 5 · ~6 hours instruction + 2.5 hour lab + breaks Prerequisite: Day 1 (Detector’s AI Stack + AI-Generated Phishing)

What Day 2 builds

Day 1 established the detector’s AI stack and applied it to AI-generated text. Day 2 applies that same stack to a fundamentally different artifact class: synthetic audio, video, and identity.

The defining property of Day 2’s threat class: the attacker’s payload is not text — it’s a deepfake voice on a Zoom call, a fabricated executive on a video conference, an AI-generated LinkedIn profile in your vendor onboarding queue. The detector’s tooling from Day 1 still applies (embeddings, RAG, hybrid retrieval) but the signal pipeline is different — and the durable controls are workflow-shaped, not artifact-shaped.

By end of Day 2, students leave with:

  1. A practical understanding of the Arup HK$200M anchor case and the subsequent 2024-2026 deepfake-BEC incident landscape
  2. A working audio deepfake detection pipeline using current open-source models
  3. The honest read on video deepfake detection: generation has outpaced detection, so layered workflow defenses are the durable controls
  4. A SIEM-grade workflow-gap detection (the “wire-transfer-changed-without-OOB-verification” pattern) that catches incidents the audio detector misses
  5. The IR playbook for a deepfake-suspected incident from first 30 minutes through regulatory notification

The six modules

#ModuleFocus
2.1Anchor case: Arup HK$200M deepfake BECThe Feb 2024 case, follow-on incidents 2024-2026, FBI/Europol trend data
2.2Synthetic audio detection — what’s catchableAASIST family + successors, current open-source detectors, working Python pipeline, known evasions
2.3Synthetic video detection — and why it’s harderC2PA adoption in 2026, physiological liveness, why detection has fallen behind generation
2.4The vishing kill chainRecent incidents, the FBI/IC3 numbers, regulatory mandates for OOB verification, where to break the chain
2.5Synthetic identity at scaleAI-generated profiles for KYC bypass, vendor fraud, executive impersonation, KYC vendor defenses
2.6IR playbook: deepfake-suspected incidentFirst 30 minutes, forensic preservation, regulatory notifications, transaction reversal windows

Lab 2

Students operate both sides in a controlled lab:

The lab is deliberately designed with a hard case: the audio detector scores the deepfake at 0.61 against a 0.7 default threshold — below alarm, but the audio is fake. Students who blindly trust the threshold miss the attack. They must demonstrate that the workflow-gap SIEM rule catches the wire transfer even when the audio detector misses — the durable control is the OOB-verification gate, not the artifact classifier.

Key references for Day 2

Verified case studies (cross-checked May 2026):

Frameworks and standards:

Tools introduced (working code in Module 2.2):

How Day 2 changes the detector’s mental model

Day 1 framed the detector’s work as artifact analysis — given an email, classify it. Day 2 introduces the lesson that for high-impact deepfake threats, artifact analysis alone is insufficient:

The durable controls are workflow gaps: a wire transfer with changed payment instructions must be confirmed through a second channel the attacker doesn’t control. The absence of that confirmation event in your SIEM is your highest-fidelity detection. Day 2 makes this explicit — and arms the detection engineer with the workflow-gap detection patterns that survive when artifact detection fails.

What Days 3-5 build on this foundation