Primer: Building Secure, Compliant & Cost-Efficient Domain-Specific LLMs for Cyber-Security & Infrastructure Teams

TL;DR
This 12 k-word field manual shows security engineers and infrastructure teams how to train, harden, and run their own LLMs—without leaking data, breaking the bank, or violating GDPR/HIPAA/FSTEC. Copy-paste configs, region-specific blueprints, and Colab-ready code included.

0. Why You Should Care

Commercial LLM APIs are toxic for high-sensitivity workloads:

The fix is Shift-Left AI:

1. Domain-Specific Training on your logs, tickets, and threat intel.
2. Integration Programming → strict JSON schemas, not prose.
3. Compliance-by-Design → pick the right region, crypto, and tenancy.
4. Cost Engineering → LoRA + spot GPUs + quantisation → 50–60 % cost cut.

1. Model Selection Matrix

Rule of thumb: start with Mistral-7B + LoRA on a T4; graduate to Llama-3-70B only if reasoning depth is poor.

2. Data Engineering Playbook

2.1 Extraction

2.2 Cleaning

pip install text-dedup langchain
python -m text_dedup.minhash \
  --path "data/raw/" \
  --output "data/dedup/" \
  --column "text"

• Remove PII with presidio-analyzer.
• Deduplicate >30 % on typical SOC dumps.
• Convert to conversational JSONL:

{"input": "SOC Alert: Brute-force on VPN (src_ip: 10.0.0.12)", "output": "{\"action\": \"block_ip\", \"target\": \"10.0.0.12\", \"confidence\": 0.92}"}

3. Fine-Tuning Recipes

3.1 LoRA (90 % of cases)

from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

• VRAM: 7 B model → 6 GB (batch=1, 4-bit).
• Speed: ~500 samples/sec on A100 80 GB.
• Convergence: 3 epochs on 10 k samples ≈ 45 min.
• Parameter delta: r × d_model × n_layers × 2 ≈ 262 k params (≈ 0.004 %).

3.2 Full Fine-Tuning (high-stakes)

Only when you need max fidelity (legal docs, medical).

3.3 Quantisation for Edge

from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16
)

• Jetson AGX Orin (32 GB GPU slice) → ~40 tok/sec for 4-bit Mistral-7B.
• Latency <500 ms for SOC chat-bot.

4. Infrastructure Overhead Cheatsheet

4.1 Public Cloud (spot pricing 2025-08)

Spot savings: 50–60 % (GPU) and up to 80 % on Azure Low-Priority VMs.

4.2 On-Prem / Air-Gapped

Physical isolation eliminates egress and compliance surface—mandatory for classified enclaves.

5. Regional Compliance Blueprints

5.1 EU GDPR – Finance Analytics

• Location: GCP europe-west4
• Storage: Cloud Storage bucket with EU_LOCATION constraint
• Compute: Vertex AI with VPC Service Controls
• Crypto: CMEK or Cloud HSM / external key (FIPS 140-2 Level 3)

5.2 HIPAA – US Healthcare

• Training: SageMaker in AWS GovCloud (us-gov-west-1)
• Inference: PrivateLink endpoint inside dedicated VPC
• PHI Redaction: Lambda layer using presidio-anonymizer
• Audit: CloudTrail + GuardDuty → Splunk

5.3 Israel Defense – Air-Gapped

• Hardware: 2×A100 80 GB, no NIC to Internet
• Stack: OpenShift + TGI container (ghcr.io/huggingface/text-generation-inference:1.4.2)
• Model Signing: GPG-sign every LoRA adapter
• Update Cycle: USB sneakernet every 30 days

5.4 China DSL – Threat Intelligence

• Provider: Alibaba PAI (Ascend 910 NPUs)
• Data Residency: MaxCompute in Beijing region
• Encryption: SM4 for data at rest, TLS 1.3 CN-specific ciphers
• Model: YaLM-100B fine-tuned on local SOC logs

5.5 Russia FSTEC – Sovereign Cloud

• Provider: Yandex DataSphere
• Encryption: GOST 28147-89
• Hardware: A100 cluster in Moscow DC
• Model: YaLM-100B or custom 70 B Llama

6. Deployment Patterns

6.1 Real-Time SOC Co-Pilot

# k8s/tgi-stack.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-triage
spec:
  replicas: 2
  selector:
    matchLabels: { app: llm-triage }
  template:
    metadata:
      labels: { app: llm-triage }
    spec:
      containers:
      - name: tgi
        image: ghcr.io/huggingface/text-generation-inference:1.4.2
        args:
          - --model-id=/mnt/models/mistral-7b-lora
          - --quantize bitsandbytes-nf4
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: 8Gi
        volumeMounts:
          - { mountPath: /mnt/models, name: model }
      volumes:
        - name: model
          persistentVolumeClaim: { claimName: pvc-model }

• Latency: p95 < 400 ms
• Auto-scale: KEDA on GPU utilisation > 80 %.

6.2 Batch Earnings-Call Pipeline

# lambda_handler.py (AWS)
import boto3, sagemaker
sess = sagemaker.Session()
model = sagemaker.model.Model(
    image_uri="763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:2.1.0-transformers4.40-gpu-py310-cu121-ubuntu22.04",
    model_data="s3://artifacts/llama3-earnings.tar.gz",
    role=role,
    sagemaker_session=sess)
model.deploy(
    initial_instance_count=2,
    instance_type="ml.g4dn.xlarge",
    endpoint_name="earnings-batch")

• Throughput: 600 calls / hour
• Cost: $0.012 per call (spot g4dn)

7. Monitoring & Guardrails

8. Cost Calculator (copy-paste)

# cost.py
def training_cost(gpus, hours, spot_discount=0.55, rate=3.06):
    on_demand = gpus * hours * rate
    return on_demand * (1 - spot_discount)

def inference_cost(req_per_month, per_1k=0.012):
    return req_per_month * per_1k / 1000

print("Training:", training_cost(8, 12), "USD")
print("Inference:", inference_cost(1_000_000), "USD/month")

9. Quick-Start Colab Notebook

https://colab.research.google.com/github/unattributed/llm-guide/blob/main/domain_llm_quickstart.ipynb

The notebook is currently in a private repo.
Request access here or clone the repo locally. Runs on free T4; fine-tunes Mistral-7B LoRA in 25 min on 5 k SOC alerts.

10. Checklist Before Go-Live

• Data cleaned + deduped
• GPU spot quota approved
• VPC-SC / PrivateLink tested
• PII filter passes pen-test
• JSON schema enforced
• Drift job scheduled (weekly)
• Cost budget + alerts set

11. Roadmap for Advanced Teams

12. References & Credits

• HuggingFace PEFT docs
• AWS “HIPAA on SageMaker” whitepaper
• Google “VPC Service Controls Best Practices”
• NVIDIA AI Enterprise Deployment Guide
• unattributed.blog threat-hunting primers

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