Topic starter
02/08/2026 12:26 pm
TL;DR: AI SOC platforms that rely only on large language models can produce inconsistent, hard-to-audit triage decisions, while deterministic tools deliver repeatable verdicts, scale, and explainability, according to Intezer. The practical lesson is that SOC automation needs both guardrails and contextual reasoning, not a single control model.
Editorial analysis by NHI Mgmt Group, based on content published by Intezer: “Why the best LLMs are not enough for the AI SOC”.
By the numbers:
- Intezer says deterministic analysis can achieve ~98% accuracy in filtering false positives at scale.
Key questions
Q: What fails when AI SOC triage relies on LLMs alone?
A: LLM-only triage fails when organisations need the same alert to produce the same decision every time.
Q: Why do deterministic controls still matter in AI SOC triage?
A: Deterministic controls matter because SOC decisions must be reproducible, explainable, and scalable under heavy alert volume.
Q: What are the signs that AI triage is too dependent on probabilistic reasoning?
A: Warning signs include inconsistent outcomes for similar alerts, analysts rechecking platform verdicts manually, and difficulty explaining why one alert was escalated while another was suppressed.
Practitioner guidance
- Define deterministic decision points Specify which alert conditions must always produce the same outcome, such as known-bad hashes, allowlists, and policy violations.
- Separate enrichment from verdicting Use LLMs to interpret context and summarize evidence, but keep final escalation and suppression logic in deterministic controls.
- Test for shallow deterministic claims Ask vendors to show whether their deterministic tools go beyond single lookups and can combine endpoint, memory, reputation, and policy evidence.
Bottom line: AI SOC triage breaks down when organisations expect probabilistic models to produce governed decisions without a deterministic backstop.
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02/08/2026 1:25 pm
Deterministic controls remain the governance anchor for SOC automation. High-volume triage needs decisions that can be repeated, reviewed, and defended. LLMs can improve interpretation, but they do not remove the requirement for a control layer that produces the same verdict every time. The practitioner conclusion is simple: if a triage decision cannot be reproduced, it is not yet governable.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: How should SOC teams balance deterministic tools and LLMs in alert handling?
A: Use deterministic tools as the decision backbone and LLMs as the reasoning overlay. Deterministic logic should handle repeatable checks, policy violations, and known indicators, while LLMs add context where intent is ambiguous. That division keeps automation defensible while still allowing deeper interpretation where it is genuinely useful.
👉 Read our full editorial: Why deterministic tools still matter in AI SOC triage