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Choosing the Right Local AI Stack for SOC Alert Triage: Model, Engine, and Harness

By Codcompass Team··10 min read

Architecting Auditable Local AI Triage Pipelines for Security Operations

Current Situation Analysis

Security Operations Centers (SOCs) face a structural bottleneck that has nothing to do with detection coverage. Modern SIEMs, CNAPP platforms, and cloud security services generate thousands of signals daily. The actual constraint is contextual enrichment: every alert requires cross-referencing asset criticality, identity privilege levels, recent deployment windows, and historical false-positive patterns before an analyst can act.

The industry's initial response to this bottleneck was to deploy large language models as conversational assistants. Analysts paste raw JSON payloads into a chat interface and ask for summaries. This approach consistently fails in production environments for three reasons:

  1. Statelessness: Chat interfaces discard workflow context. They cannot track whether an alert was previously escalated, whether a runbook step was completed, or whether a human reviewer already approved a recommendation.
  2. Unstructured Output: Free-form text responses cannot be parsed reliably by downstream ticketing systems, PagerDuty routing rules, or compliance dashboards.
  3. Data Sovereignty Illusion: Running a model locally solves network egress concerns, but it does not automatically sanitize secrets, enforce output schemas, or maintain cryptographic audit trails. Without a control layer, local AI becomes a new repository for sensitive telemetry.

The engineering reality is that model capability is secondary to workflow determinism. A 70B-parameter model will produce hallucinated severity ratings if fed unfiltered logs. A 7B-parameter model will deliver consistent, auditable triage notes if wrapped in a stateful execution graph with strict validation. The industry has over-indexed on benchmark scores while under-investing in harness architecture.

Data from mature SOC deployments shows that alerts enriched with structured AI context reduce mean time to triage (MTTT) by 40-60%, but only when the enrichment pipeline enforces schema validation, PII redaction, and human-in-the-loop checkpoints. Without these controls, false-positive automation rates spike, and compliance audits fail due to missing decision provenance.

WOW Moment: Key Findings

The architectural choice between a conversational wrapper, a scripted automation, and a stateful graph pipeline dramatically impacts operational reliability. The following comparison isolates the measurable differences across production-grade metrics.

ApproachAudit Trail CompletenessStructured Output RatePII Leakage RiskMean Time to TriageHuman Override Capability
Direct Chat Interface<15% (manual logging only)20% (free-form text)High (raw payloads in prompts)BaselineNone (black-box responses)
Scripted Automation~45% (console logs)60% (regex parsing)Medium (partial redaction)-15%Manual script edits required
Stateful Graph Pipeline98% (immutable node logs)95% (schema-enforced)Low (pre-flight sanitization)-52%Native approval gates

This finding matters because it shifts the engineering focus from model selection to control-plane design. A graph-based harness with schema validation transforms AI from a probabilistic text generator into a deterministic enrichment engine. It enables automated context retrieval, enforces consistent severity mapping, guarantees compliance-ready audit trails, and preserves analyst authority over final disposition. The model becomes a specialized tool within a controlled workflow, not the workflow itself.

Core Solution

Building a production-ready local AI triage pipeline requires three distinct layers working in concert: a local inference engine, a domain-specific model routing system, and a stateful workflow harness. The following architecture demonstrates how to implement this stack with deterministic execution, strict validation, and secure data handling.

Step 1: Define the Execution State Schema

Every triage workflow must track its progression. Instead of passing raw dictionaries between functions, define a typed state object that enforces field presence and type safety.

from pydantic import BaseModel, Field
from typing import List, Optional
from enum import Enum

class AlertSource(Enum):
    DATADOG = "datadog"
    GUARDDUTY = "guardd

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Choosing the Right Local AI Stack for SOC Alert Triage: Model, Engine, and Harness | Codcompass