AI Detection for Dormant APTs: Prince of Persia

Most companies get “APT risk” wrong because they treat it like a breaking-news problem. If a threat group goes quiet, the assumption is that it’s gone. The Prince of Persia (also tracked as Infy) case shows the opposite: a state-backed operator can look dormant for years while keeping infrastructure alive, refining tradecraft, and quietly collecting intelligence on targets that won’t make headlines.

SafeBreach’s latest reporting says this Iranian APT has been operating in some form for nearly 20 years, with activity dating back to 2004, and it’s still focused on surveillance and espionage—especially against Iranian dissidents, plus targets across Iraq, Turkey, India, Europe, and Canada. What makes this relevant for the AI in Cybersecurity series isn’t just the geopolitics. It’s the method: stealthy command-and-control, selective targeting, and cryptographic trust checks that make classic “block the domain” defenses look naïve.

The practical question for enterprise defenders is simple: how do you spot an actor that’s intentionally designed to blend into normal traffic, survive takedowns, and reappear stronger? The answer isn’t “buy more feeds.” It’s building AI-driven detection that can see weak signals across endpoints, identity, email, and network behavior—then turn those signals into decisions your SOC can act on.

Why “dormant APT” is a dangerous myth

A quiet APT is usually not inactive—it’s selective. Prince of Persia appears to run a two-stage approach that’s built for patience:

• Stage 1 triage: lightweight collection to decide if the victim is worth deeper investment.
• Stage 2 espionage: heavier tooling and longer-term data collection.

That model matches how mature operators manage cost and exposure. If you only hunt for high-volume indicators (lots of beacons, noisy malware families, widespread phishing waves), you’ll miss campaigns that optimize for low frequency and high value.

Here’s what I’ve found working with teams defending against “quiet” intrusions: the first detectable sign is often not malware at all. It’s a small behavioral mismatch—an Excel attachment that behaves like a loader, a new scheduled task with odd timing, a process tree that doesn’t fit the user’s role, or repeated outbound attempts to algorithmically generated domains.

AI-driven threat detection matters because it can correlate these mismatches even when each one looks harmless on its own.

What “still alive” really means operationally

If an actor can maintain operational infrastructure for years, you should assume three things:

1. They’re disciplined about OPSEC (minimizing artifacts, rotating infrastructure, avoiding obvious mistakes).
2. They’ve learned from past takedowns (designing controls that resist sinkholing and domain seizures).
3. They prioritize resilience over speed (slower campaigns, fewer victims, longer dwell time).

That’s exactly the pattern described here: a long-running toolset (Foudre and Tonnerre), improved stealth, and C2 designs meant to prevent defenders from hijacking communications.

The tactics that make Prince of Persia hard to catch

Prince of Persia reportedly relies on two custom tools:

• Foudre: a lightweight, first-stage implant focused on initial reconnaissance and triage.
• Tonnerre: a heavier backdoor used for deeper espionage.

What’s interesting isn’t the names. It’s the architecture choices that complicate conventional controls.

Excel delivery + low detection isn’t “just phishing”

The report notes a newer Foudre variant delivered as an executable within a Microsoft Excel file and showing no detections on VirusTotal at the time observed. That’s not magic; it’s a reminder that signature-based detection has a timing problem.

For defenders, the lesson is to stop treating Office-borne threats as a binary “macro/no macro” conversation. Mature actors will:

• Avoid obvious macros and instead rely on embedded executables, container tricks, or living-off-the-land execution.
• Keep payloads small and flexible so they can swap second-stage tooling.
• Decide quickly whether to escalate—or self-delete to reduce forensic evidence.

That “self-destruct if not valuable” behavior is a big deal. It means your incident response might find almost nothing unless your telemetry and detection are already watching the right behaviors.

Telegram as C2: common tactic, uncommon discipline

Using messaging platforms for command-and-control isn’t new. What stands out here is the reported approach: Tonnerre can use the Telegram API, but doesn’t embed the API key in the malware, and instead retrieves it only for certain victims.

Defensively, this changes what works:

• IOC hunting for hardcoded tokens gets you nowhere.
• Blocking Telegram broadly might be politically or operationally unrealistic.
• The more reliable path is behavioral detection: unusual API usage patterns, unexpected TLS destinations from non-communications endpoints, or an endpoint process (like Excel or a spawned child) initiating network flows it never should.

Cryptographic C2 trust checks: the takedown killer

The most defender-hostile detail is Foudre’s reported use of RSA signature verification combined with a domain generation algorithm (DGA) that produces roughly 100 candidate domains per week.

This is an underappreciated shift: the malware doesn’t just “phone home.” It performs a trust check so it won’t talk to a server unless that server proves it has the right private key.

That breaks a lot of standard playbooks:

• Registering predicted DGA domains doesn’t automatically help.
• Sinkholing becomes far harder because the implant refuses to trust your server.
• Even if you collect exfiltrated data, you may not be able to decrypt or validate it without the private key.

A simple but brutal idea: defenders can’t easily impersonate C2 if the malware authenticates the attacker.

This is exactly where AI-assisted detection earns its keep: you’re less dependent on “take over the domains” and more focused on identifying the infection and behavior early.

Where AI-driven threat detection changes the outcome

AI won’t “solve APTs.” But it can reduce the time between initial foothold and containment—especially for stealthy actors—by detecting patterns that are hard to express as static rules.

1) Detecting weak signals across the kill chain

Prince of Persia’s approach creates small signals across multiple layers:

• Email and file: suspicious Excel attachments, odd file structures, unusual embedded binaries.
• Endpoint: Excel spawning unexpected processes, creation of persistence artifacts, atypical DLL loads.
• Network: repeated outbound attempts to domains with algorithmic characteristics, unusual destination diversity, periodicity.
• Identity: opportunistic credential access or token use that doesn’t match the user’s normal environment.

AI-based analytics can correlate these into one story: “This endpoint opened a rare attachment type, spawned an abnormal child process, and attempted DGA-like resolution patterns afterward.” That’s the kind of narrative a human analyst can act on.

2) DGA and anomaly detection that’s actually useful

DGA detection is often sold as a checkbox and deployed as a noisy alert stream. The way to make it useful is to combine:

• Lexical domain features (entropy, n-gram frequency, length patterns)
• Temporal behavior (bursting, weekly cadence, retry logic)
• Context (what process initiated the lookup, what host role is this, what else happened 5 minutes before?)

When an implant generates 100 candidate domains a week, you don’t need to predict all domains perfectly. You need to flag the behavioral cluster early enough to isolate the host.

3) Stopping “selective targeting” with entity-based baselines

Selective campaigns exploit the fact that most environments have weak baselines. If you can’t answer “what’s normal for this user, this device, this department,” you can’t spot low-and-slow tradecraft.

AI helps by building entity behavior analytics that are practical:

• A finance user who never uses developer tools suddenly runs scripting engines.
• A kiosk device suddenly reaches out to new destinations and creates scheduled tasks.
• A user who typically signs in from two regions suddenly authenticates through an uncommon chain.

That won’t attribute the actor, but it will surface the compromise.

4) Triage automation that matches the attacker’s model

Prince of Persia uses Foudre to triage victims. Defenders should do the same—ethically and defensively—by triaging alerts with AI so analysts spend time where it matters.

A good AI-assisted SOC workflow:

1. Cluster alerts by incident storyline (not by product).
2. Score confidence using multi-signal evidence (endpoint + identity + network).
3. Recommend next steps (isolate host, collect memory, block execution chain, reset tokens).
4. Learn from outcomes (feedback loop reduces false positives over time).

This reduces dwell time, which is the only metric that really scares espionage actors.

Practical defenses you can implement this quarter

You don’t need a moonshot program to get better at catching “dormant” APT activity. You need tighter visibility, stronger baselines, and AI that’s applied to the right questions.

Focus on these high-signal detections

If you’re building a detection roadmap for state-sponsored threats, prioritize:

• Office process lineage: alerts when Excel/Word spawns cmd, powershell, wscript, rundll32, or drops executables.
• Rare file execution: first-seen binaries launched from user-writable directories (Downloads, Temp, AppData).
• DGA-like DNS behavior: high-entropy NXDOMAIN bursts, repeated attempts across many domains, unusual weekly patterns.
• Outbound from unexpected apps: Excel or a child process making TLS connections to novel destinations.
• Persistence creation: scheduled tasks, registry run keys, startup folder writes shortly after opening an attachment.

Reduce Telegram/API blind spots without blocking everything

If your organization legitimately uses Telegram (or other messaging platforms), blanket blocking is often unrealistic. Instead:

• Restrict which endpoints are allowed to run messaging clients.
• Monitor for API access patterns from non-approved hosts.
• Watch for unusual data egress volume and timing, especially after new process execution chains.

Treat “takedown resistance” as a signal, not a setback

When malware authenticates its C2 using cryptographic verification, you may not be able to intercept communications. Don’t frame that as failure.

Frame it as: “We must detect earlier in the chain.” That pushes investment toward:

• Endpoint telemetry quality (process, module, registry, network events)
• Memory capture readiness
• Automated containment playbooks
• AI-driven correlation across tools

What this means for leaders: risk, not attribution

Executives often ask, “Is this the Iran group?” The operational question is more useful: “Can we detect and contain low-noise espionage before data leaves?”

Prince of Persia is a clean example of why this matters in 2025 planning cycles. Many organizations are finalizing budgets, refreshing SOC tooling, and setting next-year KPIs right now. If your metrics are still centered on volume (alerts closed, IOCs added, domains blocked), you’ll miss the threats that don’t care about volume.

AI in cybersecurity works when it’s used to reduce uncertainty fast: identify suspicious behavior clusters, prioritize them, and automate the safe parts of response.

Next steps: how to pressure-test your readiness against “quiet APTs”

If you want a concrete starting point, run a tabletop that assumes a two-stage intrusion like Foudre → Tonnerre:

1. A user opens an Excel file.
2. A small first-stage tool inventories the system.
3. Only high-value targets get second-stage tooling.
4. C2 rotates via DGA and uses cryptographic trust verification.
5. Exfiltration blends into legitimate TLS.

Then ask your team:

• Where would we see the first reliable signal?
• How long until we isolate the endpoint?
• Can we reconstruct the process tree and outbound connections?
• Do we have automated containment that doesn’t require perfect attribution?

That exercise usually reveals the gap: it’s not that people aren’t smart. It’s that the environment doesn’t connect signals quickly.

For the AI in Cybersecurity series, Prince of Persia lands as a blunt reminder: stealth isn’t about invisibility—it’s about forcing defenders to rely on the wrong indicators. If your program is built around behavior, correlation, and fast response, a “dormant” APT doesn’t stay dormant for long.