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01/08/2026 8:57 pm
TL;DR: Agentic AI now produces a third attack rhythm between human-paced and scripted activity, and LimaCharlie argues that current velocity and path-based detections miss that middle ground because agents pause, adapt, and backtrack, according to LimaCharlie. The practical shift is toward timing variance, reasoning artefacts, and honeypot divergence as core detection signals rather than relying on fixed sequences alone.
NHIMG editorial — based on content published by LimaCharlie: Fingerprinting AI Attacks: Detection Every SOC Needs
By the numbers:
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should SOC teams detect agentic attacks that do not behave like scripts or humans?
A: SOC teams should detect agentic attacks by combining timing variance, path divergence, and reasoning artefacts in the same analytic view.
Q: Why do conversational AI systems create new identity and access risks?
A: Because they can combine data retrieval, decision-making, and execution in a single interaction.
Q: What breaks when detection rules assume attackers are either human or scripted?
A: Rules break when they assume a simple either-or model because agentic activity sits between the two.
Practitioner guidance
- Instrument timing variance at the session level Measure inter-command timing for human, script, and agentic sessions separately.
- Correlate reasoning artefacts with access events Enrich SIEM and EDR pipelines with model endpoint calls, prompt construction traces, and tool invocation patterns so analysts can connect agentic behaviour to specific authentication and privilege events.
- Add decoy resources to detection engineering Plant honeypots and other low-risk decoy assets that only an adaptive actor would inspect.
What's in the full article
LimaCharlie’s full post covers the detection engineering detail this analysis intentionally leaves for the source:
- Telemetry patterns for separating human pacing, scripted execution, and agentic reasoning in SIEM and EDR pipelines
- Honeypot design considerations for identifying adaptive behaviour without overfitting to one toolchain
- Examples of the signal types analysts can correlate to spot prompt-driven access decisions
- Implementation context for LimaCharlie’s Agentic SecOps Workspace and how it supports detection engineering
👉 Read LimaCharlie’s analysis of fingerprinting agentic attacks for SOC detection →
Agentic attack fingerprints: are SOC detections keeping up?
Explore further
01/08/2026 9:05 pm
Agentic behaviour creates a detection class that legacy SOC telemetry was never designed to see. The article correctly frames a third behavioural category between human pace and scripted speed. That matters because most logging logic is still tuned to regularity, not adaptive reasoning. Once an attacker can vary tempo and path selection based on feedback, the control problem moves from simple anomaly detection to behavioural identity detection. Practitioners should treat this as a SOC and NHI governance issue, not just an analytics tuning issue.
A question worth separating out:
Q: How can security teams measure whether agentic detection is actually working?
A: Teams can measure effectiveness by testing whether detections catch adaptive pivots, decoy investigation, and mixed-speed sessions, not just known bad commands. If an actor can move through a honeypot or vary its path without triggering an alert, the control is still too brittle for agentic threats.
👉 Read our full editorial: Agentic attack fingerprints are exposing SOC detection gaps
