WannaCry ransomware analysis: lateral movement propagation

The next intrusion into your environment may run with little or no human involvement. An agent can receive an objective, enumerate your external surface, select a target, harvest a credential, authenticate, map the internal environment, and begin moving laterally as one continuous loop. No working hours. No handoff delays. No operator required between steps. In late 2025, a disclosed state-sponsored campaign showed an AI agent executing most of the intrusion lifecycle autonomously across dozens of enterprise targets. The question is no longer whether this is technically possible. It is what changes when that operating model becomes practical.

Generative AI security, or gen AI security, has largely been framed as a content problem: better lures, faster exploit code, deepfakes. That framing is already too narrow. The more consequential shift is from humans using AI to AI pursuing objectives. Detection layers built around familiar payloads, infrastructure, and human behavior now have to contend with activity that can be novel, machine-paced, and deliberately human-plausible. That puts pressure on pattern-dependent detection and creates a different opportunity for deception: the agent still has to read, trust, and act on the environment in front of it.

At a glance

• The threat model has moved past AI-generated content. Autonomous agents can now run reconnaissance, credential harvesting, and lateral movement as a connected machine-speed loop.
• Behavioral detection was largely built around human tradecraft: work rhythms, typing cadence, and the pauses and errors of a person at a keyboard. An agent does not need to produce those tells and can be instructed to mimic many of them.
• Agents act on machine-readable signals. Deception is designed to corrupt that layer, turning the agent’s strength, broad and persistent enumeration, into a defensive advantage.
• Every deceptive asset an agent must evaluate taxes its reasoning loop: tokens, time, and confidence. Deception can degrade the attack even before a deceptive asset is touched.
• In the U.S. Navy’s ANTX FY25 exercise, Acalvio’s deception technology delivered 100% true positive alerts and denied 80% of attacker objectives against automated, credential-driven intrusion techniques.

Generative AI security risks are not one phenomenon. They span three distinct rungs of attacker maturity, while most gen AI security programs are still calibrated to the first.

AI-assisted. A human operator runs the intrusion and uses a model to accelerate the expensive parts: fluent spear-phishing lures generated per target, proof-of-concept exploit code drafted straight from a CVE description, and deepfake audio and video for social engineering. This is where much of the market conversation has focused for the past two years. It matters, but it is the least consequential of the three rungs.

AI-orchestrated. A human sets the objective and an agent executes the chain: reconnaissance, target selection, initial access, and privilege escalation. The human checks in at decision points. The disclosed 2025 espionage campaign operated here, with the agent performing the large majority of tactical work at a pace and breadth difficult for a human team to match.

Autonomous. The agent runs the full lifecycle against the objective with no human in the loop. Red-team research has already demonstrated agents completing multi-step intrusions end to end in lab environments. Defenders should not count on a long gap between lab capability and criminal adoption.

The question worth asking of your current stack is not whether it handles rung one. It is what happens at rungs two and three, where the adversary stops behaving like the human actor many controls were designed to model.

Many detection controls share one buried assumption: a threat will resemble something already seen or meaningfully deviate from normal. Signatures assume known payloads. Email reputation assumes reused infrastructure. Behavioral analytics look for departures from an expected baseline. Each still has value. The problem begins when an agent can generate new activity, adapt its pace, and operate through valid access and legitimate tools. That is where the operating model starts to strain.

The behavioral layer faces a particular challenge. UEBA baselines and EDR behavioral models are built to identify departures from expected human and system behavior: login patterns that follow time zones, command sequences with the pauses and typos of a real operator, and lateral movement paced by a person thinking through next steps. An agent does not need to produce those artifacts natively, and it can be instructed to reproduce some of the signals tooling expects. Valid identity plus legitimate tooling plus human-plausible cadence can read as authorized activity.

This is an architecture problem, not just a tuning problem. Tuning still matters, but it does not fully address a control model that depends on recognizing an adversary whose behavior can be novel, adaptive, and deliberately plausible.

Here is the part the AI threat narrative often misses: this dynamic can run in the defender’s favor.

Deception has never depended on the attacker being careless. A well-constructed deceptive asset is designed to be indistinguishable from production at the data layer, which is why deceptive credentials and decoys have exposed human intruders for years, going back to the honeypot era. Caution can slow an operator down. It does not solve the information problem when the real and the deceptive are equally convincing.

An agent inherits that same information problem and adds two liabilities of its own. First, it acts on the machine-readable layer: directory entries, credential material, service banners, and network paths. That is precisely the layer deception can manipulate, so the agent may plan its next move on corrupted information. Second, agents can enumerate far more broadly because enumeration is cheap at machine speed. That increases the odds of contact with a deceptive asset and can sharply compress the time to exposure. The agent’s core advantage, tireless breadth, becomes a mechanism of accelerated exposure.

This is where Acalvio’s three-vector framework stops being only a detection story and becomes an attack-degradation story:

Fake Looks Real seeds deceptive credentials, decoys, and HoneyPaths that corrupt the machine-readable map an agent builds during reconnaissance. The agent may plan its next move on false information. Use of a deceptive credential in Active Directory, cloud directories, or along lateral-movement paths provides a high-confidence signal of intent.

Real Looks Fake makes production assets appear deceptive, which can force the agent to distrust legitimate targets. This can degrade the attack even when nothing deceptive is touched: the agent may hesitate, deprioritize, or route around assets it should have exploited.

Intentionally Suspicious artifacts cannot be safely dismissed, so the agent may spend cycles verifying them. Every verification burns compute, time, and confidence. That creates a second effect beyond detection: the reasoning loop itself becomes more expensive. An agent forced to verify more and trust less gives up some of the speed advantage that made it dangerous in the first place.

Pattern-based tools ask some version of, “have I seen this before?” Deception asks a different question: “why did anything touch an asset no legitimate process should need?” That difference matters when the adversary’s behavior is new, adaptive, or machine-generated.

The practices most likely to hold up are the ones that do not depend entirely on recognizing the attack. Layer these on top of the email security, EDR, IAM, and PAM you already run; none replaces an existing control.

1. Seed deceptive credentials across Active Directory and cloud directories so any use provides a high-confidence signal of credential misuse, whatever generated the tooling behind it.
2. Instrument lateral-movement paths toward critical assets with decoys and HoneyPaths, converting a quiet pivot into a high-fidelity alert.
3. Treat AI-generated content and activity as unverified until it interacts with your environment, where deceptive assets can provide a verification point that does not depend on spotting a familiar pattern.
4. Feed deception signals into your SIEM as confirmed-malicious context rather than one more anomaly in the triage queue.
5. Emulate agentic adversaries, not just AI-written phishing, so you can learn whether your controls hold against machine-speed campaigns before a live one runs.

Deception earned its skeptics in the honeypot era, when decoys were hand-built, hand-fed, and stale within a quarter, generating alerts nobody had routed. That objection deserves a direct answer: against a machine-speed adversary, that operating model fails the test. An agent that enumerates an environment in minutes may also identify a neglected decoy far faster than a human operator would. The practical requirement is deception that can be deployed, diversified, and refreshed at comparable speed. That is what ShadowPlex automates across Active Directory, cloud directories, and lateral-movement paths, with deception signals feeding the SIEM as confirmed-malicious context rather than one more anomaly in the triage queue. Automation is not a convenience feature here. It is the entry requirement for operational credibility at machine speed.

In the U.S. Navy’s ANTX FY25 exercise, Acalvio’s deception technology was tested against automated, credential-driven intrusion techniques within the exercise environment and delivered 100% true positive alerts while denying 80% of attacker objectives. Credential-based intrusion remains the path behind roughly four out of five breaches in Verizon’s DBIR analysis, and it is a path agentic attacks are likely to exploit because valid credentials and lateral movement remain efficient ways to reach valuable assets. Agentic AI accelerates that path. It does not change the destination. Deception sits on the path itself.