AI Detection Lessons From Iran’s “Dormant” APT

A threat actor that’s been active since 2004 shouldn’t be able to “disappear” for three years—unless your defenses are mostly tuned for loud, obvious attacks.

That’s what makes the reappearance of Iran’s long-running APT known as Prince of Persia (also called Infy) so useful as a case study for this AI in Defense & National Security series. The group didn’t vanish. It got quieter, upgraded its tradecraft, and kept doing what nation-state operators do best: targeted espionage, careful victim selection, and resilient command-and-control.

If your security program is still built around signatures, “known bad” indicators, and periodic threat-hunting sprints, this story should feel uncomfortably familiar. The real lesson isn’t “Iran has another APT.” The lesson is that long-dormant APT activity is exactly where AI-driven threat detection earns its keep—by finding patterns that don’t look malicious at first glance.

Prince of Persia proves “quiet” doesn’t mean “gone”

Answer first: “Dormant APT” often means low-noise operations that slide under traditional detection, not an inactive adversary.

According to recent research, Prince of Persia has continued operating while attracting far less attention than higher-profile Iranian groups. Its reported targeting includes Iranian citizens and dissidents, with additional activity spanning Iraq, Turkey, India, Europe, and Canada. That mix matters for enterprises and public sector organizations because it overlaps with:

• diaspora communities and advocacy groups
• universities and research institutions
• NGOs and media organizations
• government agencies and contractors

These campaigns tend to be selective. The attacker’s goal isn’t ransomware-scale impact. It’s quiet access, credential harvesting, device monitoring, and file collection.

Why defenders keep missing “slow burn” espionage

Most companies get this wrong: they treat APT detection as a tooling problem (“we bought EDR”) instead of a visibility and analysis problem (“we can’t connect weak signals across time”).

State operators exploit that gap by:

• stretching campaigns over months to blend into normal activity
• using benign or dual-use channels (messaging APIs, cloud services)
• keeping payloads small and modular (only escalate on valuable targets)

That’s the exact detection niche where AI in cybersecurity can outperform rule-heavy approaches—because the job is less about “spot this hash” and more about “spot this behavior.”

The tradecraft that makes this APT hard to disrupt

Answer first: Prince of Persia’s newer tooling focuses on C2 resilience and verification, limiting what defenders can hijack, analyze, or sinkhole.

Two malware families stand out in reporting: Foudre (lightweight reconnaissance/triage) and Tonnerre (heavier espionage). That split is common among mature APTs: first identify whether a victim is interesting, then deploy deeper capability.

Foudre: triage first, persistence second

Foudre’s role is to collect basic system information and decide whether the operator should invest further. A tactic described in research is particularly telling: after initial infection, the actor either:

• escalates to more involved tooling, or
• sends a command for the implant to self-destruct

Self-destruct workflows reduce forensic evidence and starve defenders of samples.

From a detection standpoint, triage implants are annoying because they can look like:

• a short-lived execution event
• a small amount of outbound traffic
• minimal file system footprint

That’s not enough for many alerting pipelines to fire.

Tonnerre: using Telegram without leaving easy clues

Tonnerre reportedly can use the Telegram API for command-and-control—common enough that defenders may assume it’s straightforward to hunt for. The twist is more subtle: rather than embedding an API key in the malware (which researchers can extract), the key is retrieved only for specific victims.

This matters operationally:

• It limits “bulk” discovery. Not every sample reveals the same secrets.
• It reduces the chance defenders can map the operator’s Telegram infrastructure.
• It increases attacker confidence that C2 won’t be burned quickly.

For enterprise defenders, the implication is simple: you can’t rely on static artifacts alone. You need behavioral analytics that flags unusual Telegram/API usage patterns on endpoints and servers that shouldn’t be using them.

The C2 trick defenders should take seriously: cryptographic trust

Foudre’s command-and-control protection is the part I want every SOC leader to internalize.

The described mechanism combines:

• a domain generation algorithm (DGA) producing many candidate domains
• a public key embedded in the malware
• an RSA signature verification process that ensures the C2 is authentic

Practical impact: even if a defender reverse engineers the DGA and pre-registers domains to sinkhole traffic, the malware won’t “trust” the fake C2 unless it can validate the signature.

This is a defensive nightmare because sinkholing is one of the cleanest ways to:

• measure infected populations
• capture traffic for analysis
• break attacker control at scale

When an APT uses cryptographic verification like this, disruption becomes a key-management problem, not a DNS problem.

What this means for AI in threat detection (and why it’s not optional)

Answer first: AI-powered security monitoring helps because it correlates weak signals across endpoints, identity, network, and time—exactly the conditions “dormant” APTs depend on.

To be clear: AI doesn’t magically decrypt RSA or “solve” sinkholing. The win is earlier visibility, faster triage, and better containment when you can’t rely on takedowns.

Here’s how AI-driven threat detection maps to this case.

1) Catch the quiet first-stage behavior

Foudre-like implants are built to be low-noise. That pushes defenders toward anomaly detection and sequence-based analytics, such as:

• rare parent/child process chains (Office → dropped EXE → network beacon)
• “one-time” binaries executed on a user workstation
• abnormal outbound connections shortly after document open

AI models can score these as suspicious even when no signature hits.

What works in practice is using AI to generate high-confidence leads, then letting analysts validate with traditional forensics.

2) Detect “legit service” abuse without blocking the internet

If an APT uses Telegram, cloud storage, or other legitimate services, you’re not going to block them across a whole enterprise without breaking business workflows.

AI helps by baselining what’s normal:

• Which teams use Telegram (if any)?
• From which devices and geographies?
• At what times?
• With what data volumes?

When an endpoint that never used Telegram suddenly begins periodic API calls after an Office execution chain, you’ve got a strong, explainable detection.

3) Turn threat intelligence into detections faster

A common failure mode: a threat intel report arrives, the SOC skims it, and nothing changes in monitoring.

AI-assisted detection engineering can shorten that loop by:

• extracting behaviors (not just indicators) from narrative reporting
• proposing hunt queries and detection logic
• prioritizing telemetry sources that would confirm the technique

In other words: AI can convert “interesting read” into “deployed detection” while the campaign is still active.

4) Improve triage when the attacker self-destructs

Self-destruct commands are designed to leave you with a few scraps: short process traces, a bit of network metadata, and maybe a suspicious file open event.

AI can still help by correlating:

• identity signals (new token usage, impossible travel, MFA fatigue patterns)
• endpoint execution sequences
• DNS and TLS fingerprint anomalies
• similar events across other users (campaign clustering)

Even if the implant is gone, the pattern remains.

A practical playbook: how to prepare for long-dormant APT activity

Answer first: You don’t “buy” your way out of this; you build a detection loop that blends AI analytics with disciplined security operations.

Here’s a concrete approach I’ve found works for teams trying to defend against state-level cyber espionage without boiling the ocean.

Step 1: Define your “APT-relevant” crown jewels

Prince of Persia reportedly focused on dissidents and individuals of interest. In enterprise terms, that maps to:

• executive communications and legal strategy
• journalist/activist safety information
• travel plans, case files, immigration documentation
• sensitive HR investigations
• research partnerships and funding data

If you can’t list the top 10 data sets you’d least want exfiltrated, your monitoring will be generic—and APTs love generic.

Step 2: Instrument for behavior, not just alerts

Minimum telemetry that enables AI-driven threat hunting:

• process creation and command-line logging
• Office child-process monitoring
• DNS logs (including NXDOMAIN and DGA-like patterns)
• outbound proxy logs with user/device attribution
• identity provider logs (MFA, token grants, risky sign-ins)

Then make sure the SOC can ask: “What else happened on this device within ±30 minutes?” AI correlation is only as good as the data feeding it.

Step 3: Build detections around verification and selection

This APT’s architecture suggests two behaviors defenders can target:

1. Victim selection (triage + follow-on payload)
2. C2 verification (signature checks, repeated domain attempts)

Detection ideas that don’t require decrypting anything:

• repeated failed outbound connections to algorithmically generated domains
• weekly “domain churn” patterns from the same endpoint set
• bursts of DNS queries followed by a single successful connection
• Office-delivered executables that immediately perform network verification behavior

AI models can score these sequences, then analysts confirm whether it’s malware, misconfiguration, or a weird app.

Step 4: Automate containment for high-confidence chains

For suspected espionage activity, speed matters. Once you have a high-confidence chain (Office execution → dropped binary → beaconing), your playbook should support:

• isolating the endpoint
• revoking tokens and forcing re-auth
• collecting memory and disk artifacts
• blocking outbound patterns temporarily (device-scoped if possible)

AI should reduce the time-to-decision, not replace the decision.

People also ask: “Could AI have caught this years earlier?”

Answer first: Yes—if AI was used for continuous behavior monitoring and correlation, not just as an add-on dashboard.

The “dormant for years” perception is often a visibility illusion. When adversaries keep payloads small, rotate infrastructure, and use trusted channels, signature-based defenses tend to detect only the noisiest moments.

AI’s advantage is persistence: it watches everything, scores patterns, and remembers what happened last month. That’s why it fits so naturally in defense and national security settings where adversaries play the long game.

What to do next if you’re responsible for detection

Prince of Persia is a reminder that nation-state tradecraft ages well. Operators refine what works, keep what’s resilient, and re-enter the spotlight only when researchers catch up.

If you’re building a modern SOC—especially in government, critical infrastructure, higher education, or any organization with politically sensitive users—treat this as your prompt to pressure-test three things: (1) your behavioral visibility, (2) your ability to correlate weak signals, and (3) your speed from detection to containment.

If you want a practical starting point, focus on one measurable outcome: reduce the time it takes to detect a low-noise first-stage implant from days to hours using AI-assisted correlation and a tight response playbook. What would your team find if you ran that test this week?