AI Detection Lessons From Iran’s “Prince of Persia” APT

A threat group can be “quiet” for three years and still be actively spying the entire time. That’s the uncomfortable lesson from new reporting on Iran’s long-running state-backed actor known as Prince of Persia (also tracked as Infy), a campaign history that stretches back to 2004. Researchers now describe it as continuously operational, targeting Iranian dissidents and other individuals across Iraq, Turkey, India, Europe, and Canada, with upgraded malware and unusually disciplined command-and-control (C2).

For defenders, the story isn’t just “another APT is back.” It’s a case study in why AI-driven threat detection has become non-negotiable in AI in defense and national security programs: if your controls depend on loud indicators, commodity malware signatures, or occasional incident spikes, a patient espionage operation can sit inside your blind spots for years.

Prince of Persia proves “dormant” is often a detection failure

Prince of Persia didn’t vanish—it reduced its visibility. That distinction matters because most security programs still treat threat activity as something you either see (alerts) or don’t see (silence). Mature APTs exploit that assumption.

Here’s what defenders should take from the case:

• Low-volume operations beat alert thresholds. A group can run a small set of carefully selected intrusions, stay under the noise floor, and still achieve strategic intelligence goals.
• A long-lived toolkit can be a feature, not a weakness. We often assume attackers must constantly rotate infrastructure and malware. This actor shows the opposite: keep the arsenal stable, but harden the operational security around it.
• “No news” isn’t the same as “no activity.” If your threat intel program equates public reporting with real-world prevalence, you’ll miss the quiet operators.

This matters in defense and national security contexts because dissident surveillance and cross-border targeting typically follow geopolitical pressure cycles—exactly the sort of activity that peaks and dips without producing a steady stream of public breadcrumbs.

The tradecraft: stealthy staging malware and C2 that resists takedown

Prince of Persia reportedly relies on two custom malware families: Foudre (a lightweight first stage) and Tonnerre (a heavier espionage tool). On paper that sounds ordinary. The details aren’t.

Foudre: “triage-first” intrusion design

The newest Foudre variant is described as being delivered as an executable inside a Microsoft Excel file, used to collect basic system information and determine whether the target is worth deeper investment.

This “triage-first” pattern is a common espionage playbook, but many organizations still defend as if every endpoint infection will immediately trigger obvious follow-on behavior.

A practical implication for security teams:

• You need detection for pre-espionage selection behavior, not only for later-stage data theft.
• Your telemetry has to capture “small” anomalies—odd Excel child processes, unusual execution chains, short-lived binaries that self-delete.

Tonnerre: using Telegram API without leaving easy artifacts

The reported Tonnerre evolution includes C2 via the Telegram API, but with a twist: instead of embedding an API key (a common mistake that helps defenders track and disrupt), it retrieves the key only for specific victims.

That changes the defender’s job in two ways:

1. Static analysis gives you less. You can’t rely on reversing one sample and extracting a durable token.
2. Detection shifts to behavior and environment. You’re looking for unusual network patterns, abnormal process communication, and endpoint behaviors that don’t match business use.

If your organization permits Telegram in certain regions or roles, this becomes harder. “Block the app” is rarely a universal answer, especially in globally distributed environments.

Foudre’s C2 trust model: cryptographic verification + DGA

One of the most defender-relevant details is the reported use of RSA signature verification inside the malware’s C2 routine. The idea is simple and brutal: even if a researcher or defender pre-registers or sinkholes domains generated by a domain generation algorithm (DGA), the malware won’t talk unless the server presents a response signed with the attacker’s private key.

If an implant verifies the C2 cryptographically, domain takedowns and sinkholes become far less effective.

This is a meaningful shift for enterprises that still treat sinkholing and domain seizures as the “end game” for disruption. It’s still useful—just not sufficient.

What this means for AI in defense & national security

AI in defense and national security isn’t only about drones, autonomy, or intelligence fusion. It’s also about persistent cyber defense against actors whose whole strategy is to look like nothing is happening.

Prince of Persia is a clean example of why AI-based security analytics wins in the long run:

• The signal is weak (few victims, careful targeting)
• The artifacts are ephemeral (self-destruct, staged tooling)
• The infrastructure is resistant (cryptographic trust, DGA)

So what should AI actually do here? Not “detect Iran.” The value is narrower and more practical: detect the seams—where real user behavior and real system behavior diverge.

Where AI helps most: the four detections that catch “quiet APTs”

AI is at its best when it’s identifying relationships and deviations that rules miss. For dormant or low-visibility APTs, I’ve found you get the most leverage from these four detection categories.

1) Behavioral baselines for execution chains (not just processes)

Instead of flagging excel.exe or a specific hash, model the execution chain:

• Office application launches an unexpected child process
• Unusual command-line patterns
• Short-lived binaries in temporary or user-writable locations

Actionable move:

• Train or tune models on parent-child process graphs by department and role (finance vs. engineering will differ).
• Alert on new chains that appear in low frequency but repeat across a handful of endpoints.

2) Anomaly detection for “rare-but-consistent” network flows

APT traffic is often low bandwidth and consistent—the opposite of ransomware.

Actionable move:

• Build models that look for beaconing-like periodicity even when the destination rotates (DGA-like behavior).
• Add features like time-of-day regularity, TLS fingerprint changes, and destination novelty (new domains never seen in your environment before).

3) Entity resolution across weak signals (identity + endpoint + SaaS)

A single weak alert is noise. Three weak alerts across identity, endpoint, and SaaS is a story.

Actionable move:

• Use AI correlation to link: unusual login risk + Office spawning anomaly + suspicious outbound call patterns.
• Prioritize investigations when multiple low-severity events cluster around the same person or device.

This is especially relevant for dissident surveillance, where the attacker’s objective may be mailbox access, contact graphs, and document collections—not disruptive impact.

4) Attribution support: clustering tradecraft, not guessing flags

AI shouldn’t “declare attribution.” It should cluster campaigns using repeatable TTP patterns:

• Staging/triage tool behavior
• Cryptographic verification routines
• DGA characteristics
• Messaging-platform C2 patterns

Actionable move:

• Maintain an internal library of tradecraft embeddings (a structured representation of behaviors) so you can say, “This looks like the same operator set we saw last year,” even when indicators changed.

Defensive playbook: what to implement in the next 30 days

Most teams don’t need a new platform to get better at this. They need sharper questions and cleaner telemetry.

Step 1: tighten Office-to-process visibility

If you can’t reliably answer “What does Excel spawn in our environment?”, you can’t catch triage implants.

• Ensure endpoint telemetry captures process lineage and command lines
• Create allowlists for expected Office add-ins and scripting tools
• Investigate endpoints with repeated Office child-process anomalies, even if each event seems minor

Step 2: treat messaging apps as potential C2 paths

Telegram, Slack, Discord, and similar services can all be abused. The defense isn’t panic; it’s governance and monitoring.

• Define where messaging apps are approved (roles, regions, devices)
• Monitor for suspicious API-like traffic patterns from endpoints that shouldn’t be using them
• Segment egress where feasible (especially for privileged workstations)

Step 3: improve your DGA resilience

If your detection strategy is “block the bad domain,” you’re playing on the attacker’s home field.

• Add DGA-oriented analytics (domain entropy, NXDOMAIN spikes, rotating destinations)
• Track newly observed domains and their first-seen endpoints
• Hunt for periodic connections even when domains change

Step 4: run one targeted hunt per quarter for “silent espionage”

A predictable schedule helps. Pick a theme and run it like a fire drill.

Example hunt hypotheses:

1. “Office processes spawning executables from user-writable paths”
2. “Rare outbound destinations contacted by fewer than 3 endpoints, repeatedly”
3. “Endpoints with short-lived binaries followed by credential access anomalies”

People also ask: can AI detect threats that go silent for years?

Yes—if you instrument the environment well and tune models toward deviations, not signatures.

AI won’t magically surface a nation-state implant if you don’t collect the right signals. But with endpoint process graphs, network flow metadata, and identity telemetry, AI can reliably flag the kinds of weak patterns that “quiet APTs” depend on.

The real trick is operational: your SOC has to treat low-confidence anomalies as worth clustering and revisiting, not closing as noise.

The uncomfortable stance: disruption isn’t guaranteed, so detection must be continuous

Prince of Persia reportedly survived earlier disruption pressure and adapted with stronger architecture. That’s the point. APT defense is a long game, and disruption outcomes can be constrained by jurisdiction, infrastructure complexity, and (sometimes) state support.

If you work in defense-adjacent sectors, NGOs, media, critical infrastructure, higher education, or any organization that holds politically sensitive data, the smart bet is this: assume at least one actor is optimizing for stealth over speed.

What I’d do next is straightforward: validate that your AI-driven detection is catching low-and-slow behaviors, and that your incident response team can investigate them quickly.

If a 20-year-old threat group can stay “quiet” while actively surveilling people, the question for 2026 planning isn’t whether you’ll face stealthy cyber espionage—it’s whether your detection program is built to notice it.