From Deepfakes to Help Desk Scams: Detect and Defend Against AI-Driven Social Engineering

AI-Powered Cyberattacks & Social Engineering: How to Detect and Defend

"Quit thinking of deepfakes or voice AI as a Hollywood special effect. We're one step away from a help desk ticket waiting to happen." That warning comes from Adam Keown, CISO at Eastman. He's right. Generative tools have dropped the barrier to entry so low that casual attackers can impersonate your people, your brand, and your processes with alarming accuracy.

The result: scaled phishing, convincing voice clones, and synthetic identities that slip past rushed reviews. Your users won't always spot it. Your controls won't always flag it. You need layered detection and fast confirmation loops.

What's changed

• Scale and speed: AI writes thousands of unique lures in minutes.
• Believability: Voices, faces, and writing styles match your execs and vendors.
• Automation: End-to-end attack chains: scraping, staging, phishing, and follow-up.

How organizations are fighting back

Doppel is arming teams with the same tools attackers use - safely - to test and harden their defenses. "We're giving you the tools that the bad guys have to test your organization," says CEO Kevin Tian. Their platform protects dozens of Fortune 500 companies and brands like Coinbase, Ramp, Commerce, and Orrick, across financial services, energy, technology, industrials, healthcare, and media.

Detecting AI-driven social engineering

• Email/SMS: Look for urgent tone, payment or MFA requests, odd sending domains, fresh display names, and links that redirect through open trackers.
• Voice and video: Deepfakes often push speed: "I'm boarding, do this now." Watch for odd breathing, flat intonation, mismatched lighting, or frame glitches.
• Domain and brand abuse: Monitor lookalike domains, fake executive profiles, and cloned login pages. Set alerts for typosquats and homoglyphs.
• Vendor fraud: Verify banking changes with a known-good phone number. No exceptions.
• Account takeover signals: Unfamiliar devices, new MFA enrollments, consent grants to risky OAuth apps, and impossible travel.
• Source code/collab: Treat unsolicited "patches," dependency updates, or AI-shared snippets as suspect. Validate before merge.

Controls that actually help

• Email authentication: Enforce SPF, DKIM, and DMARC at p=reject. Quarantine first if needed, then move to reject.
• Phishing-resistant MFA: Prefer FIDO2/passkeys over OTP codes and push approvals. Kill push fatigue with number matching and rate limits.
• Strong verification: For payments, credentials, and access changes, require out-of-band callbacks using a directory-verified number.
• Identity and access: Least privilege, time-bound access, step-up auth on risky events, and fast offboarding.
• Brand monitoring + takedown: Track lookalikes, fake apps, and spoofed profiles. Automate takedowns and blocklists.
• Content provenance: Prefer assets with verifiable signatures (C2PA-style) where possible. Treat unverifiable media with caution.
• Detection engineering: Alert on sudden executive outreach patterns, mass vendor emails, and unusual help desk requests.

For developers shipping AI and automation

• Threat model prompts, tools, and data flows. Review against the OWASP Top 10 for LLM Applications.
• Constrain model tools and outputs. Enforce strict allowlists, rate limits, and output validation.
• Separate secrets and tokens from prompts. Rotate aggressively. Log every tool call.
• Lock down dependencies and package publishing. Verify maintainer identity and use signed artifacts.
• Guard against prompt injection, data exfiltration, and jailbreaking in user-facing features.

Train your people (short, frequent, real)

• Monthly micro-drills: email, SMS, voice, and video impersonation.
• Teach a simple rule: "Stop. Verify out-of-band. Then act."
• Give help desk scripts for executive voice clones and urgent reset requests.
• Share practical references like CISA guidance on deepfakes.

If your team needs structured upskilling on AI fundamentals and safe usage, check our latest AI courses.

Simulate like the adversary

Don't wait for a live incident to discover gaps. Use offensive testing to spot weak approvals, spoofable processes, and blind spots in monitoring. Platforms like Doppel help teams stage realistic takedowns, brand abuse tests, voice-clone drills, and executive impersonation scenarios - then fix what the simulation exposes.

Quick checklist

• Enforce DMARC p=reject and phishing-resistant MFA.
• Turn on number-matching and block MFA push spam.
• Require call-back verification for payments and access changes.
• Monitor lookalike domains and automate takedowns.
• Audit executive communication patterns and alerts.
• Lock down IT help desk identity verification flows.
• Threat model LLM features and follow the OWASP LLM guidance.
• Run monthly deepfake and social engineering drills.
• Measure time-to-detect, time-to-verify, and time-to-contain.
• Practice incident response with realistic AI-augmented scenarios.

Watch the video

A recent discussion featuring Fortune 500 leaders and Doppel's approach is available on the Cybercrime Magazine YouTube channel. Watch the video.