LLM Fine-tuning LoRA Compliance Automation

LLM LoRA Compliance

LLM Fine-tuning LoRA QLoRA Compliance Automation PCI-DSS SOC2 GDPR ISO27001 Policy Check Report Generation

LoRA Training

# === LoRA Fine-tuning with PEFT ===

# pip install transformers peft datasets bitsandbytes accelerate trl

# from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
# from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
# from trl import SFTTrainer
# from datasets import load_dataset
# import torch
#
# # 4-bit Quantization config (QLoRA)
# bnb_config = BitsAndBytesConfig(
#     load_in_4bit=True,
#     bnb_4bit_quant_type="nf4",
#     bnb_4bit_compute_dtype=torch.bfloat16,
#     bnb_4bit_use_double_quant=True,
# )
#
# # Load base model
# model_name = "mistralai/Mistral-7B-Instruct-v0.2"
# model = AutoModelForCausalLM.from_pretrained(
#     model_name,
#     quantization_config=bnb_config,
#     device_map="auto",
# )
# tokenizer = AutoTokenizer.from_pretrained(model_name)
#
# # LoRA config
# lora_config = LoraConfig(
#     r=16,                    # Rank (4-64, higher = more capacity)
#     lora_alpha=32,           # Scaling factor
#     target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
#     lora_dropout=0.05,
#     bias="none",
#     task_type="CAUSAL_LM",
# )
#
# model = prepare_model_for_kbit_training(model)
# model = get_peft_model(model, lora_config)
# model.print_trainable_parameters()
# # trainable: 4,194,304 || all: 7,245,017,088 || 0.058%

from dataclasses import dataclass

@dataclass
class LoRAParam:
    param: str
    value: str
    description: str
    tuning_tip: str

params = [
    LoRAParam("r (rank)", "8-64",
        "Rank ของ Low-rank Matrix ยิ่งสูง capacity มาก",
        "เริ่ม r=16 เพิ่มถ้า Underfit ลดถ้า Overfit"),
    LoRAParam("lora_alpha", "16-64",
        "Scaling factor ปกติ = 2×r",
        "alpha=32 สำหรับ r=16"),
    LoRAParam("target_modules", "q_proj, v_proj, ...",
        "Layer ที่จะใส่ LoRA Adapter",
        "q_proj + v_proj ขั้นต่ำ เพิ่ม k_proj o_proj ถ้าต้องการ"),
    LoRAParam("lora_dropout", "0.0-0.1",
        "Dropout สำหรับ Regularization",
        "0.05 สำหรับ Dataset เล็ก 0.0 สำหรับ Dataset ใหญ่"),
    LoRAParam("learning_rate", "1e-4 - 3e-4",
        "Learning Rate สำหรับ LoRA Training",
        "2e-4 เป็นค่าเริ่มต้นที่ดี"),
    LoRAParam("epochs", "1-5",
        "จำนวนรอบ Training",
        "3 epochs สำหรับ Dataset 1K-10K samples"),
]

print("=== LoRA Parameters ===")
for p in params:
    print(f"  [{p.param}] Value: {p.value}")
    print(f"    Description: {p.description}")
    print(f"    Tip: {p.tuning_tip}")

Compliance Dataset

# === Dataset Preparation ===

# Dataset format (Alpaca style)
# {
#   "instruction": "ตรวจสอบว่า configuration นี้ comply กับ PCI-DSS Requirement 2.2",
#   "input": "password_policy:\n  min_length: 6\n  require_special: false\n  max_age_days: 180",
#   "output": "NON-COMPLIANT\n\nFindings:\n1. min_length: 6 ต่ำกว่า requirement (≥7 characters)\n2. require_special: false ต้องมี complexity requirement\n3. max_age_days: 180 เกิน requirement (≤90 days)\n\nRemediation:\n- เพิ่ม min_length เป็น 7+\n- เปิด require_special: true\n- ลด max_age_days เป็น 90"
# }

@dataclass
class ComplianceTask:
    task: str
    input_type: str
    output_type: str
    dataset_size: str
    example: str

tasks = [
    ComplianceTask("Policy Check",
        "Configuration YAML/JSON",
        "Compliant/Non-compliant + Findings",
        "500-2000 samples",
        "ตรวจ Password Policy ตาม PCI-DSS"),
    ComplianceTask("Document Review",
        "Policy Document text",
        "Coverage Analysis + Gaps",
        "200-500 samples",
        "ตรวจว่า Information Security Policy ครบ ISO27001"),
    ComplianceTask("Q&A",
        "คำถาม Compliance",
        "คำตอบอ้างอิง Framework",
        "1000-5000 samples",
        "PCI-DSS Req 3.4 กำหนดอะไร → อธิบาย"),
    ComplianceTask("Report Generation",
        "Audit Findings list",
        "Compliance Report draft",
        "100-300 samples",
        "สร้าง SOC2 Report จาก Findings"),
    ComplianceTask("Log Analysis",
        "Security Log entries",
        "Violation Detection + Classification",
        "1000-5000 samples",
        "ตรวจ Access Log หา Unauthorized Access"),
]

print("=== Compliance Tasks ===")
for t in tasks:
    print(f"  [{t.task}] Dataset: {t.dataset_size}")
    print(f"    Input: {t.input_type}")
    print(f"    Output: {t.output_type}")
    print(f"    Example: {t.example}")

Production Pipeline

# === Compliance Automation Pipeline ===

@dataclass
class PipelineStage:
    stage: str
    tool: str
    integration: str
    output: str
    alert: str

pipeline = [
    PipelineStage("Config Scan",
        "LoRA Model + Policy Rules",
        "CI/CD Pipeline (GitHub Actions / GitLab CI)",
        "Compliance Report per PR",
        "Non-compliant finding → Block PR"),
    PipelineStage("Log Monitoring",
        "LoRA Model + SIEM Integration",
        "Splunk / Elasticsearch Webhook",
        "Violation Detection real-time",
        "Critical Violation → PagerDuty Alert"),
    PipelineStage("Document Review",
        "LoRA Model + Document Parser",
        "Scheduled Monthly Scan",
        "Coverage Report + Gap Analysis",
        "New Gap Found → Jira Ticket"),
    PipelineStage("Evidence Collection",
        "LoRA Model + API Integrations",
        "AWS Config / Azure Policy / GCP SCC",
        "Evidence Package per Control",
        "Missing Evidence → Task Assignment"),
    PipelineStage("Report Generation",
        "LoRA Model + Template Engine",
        "Quarterly Compliance Report",
        "Draft Report for Review",
        "Report Due → Reminder to GRC Team"),
]

# Serving config
# vLLM:
# python -m vllm.entrypoints.openai.api_server \
#   --model mistralai/Mistral-7B-Instruct-v0.2 \
#   --enable-lora \
#   --lora-modules compliance-lora=/path/to/adapter \
#   --max-loras 4 \
#   --port 8000

print("=== Compliance Pipeline ===")
for s in pipeline:
    print(f"  [{s.stage}] Tool: {s.tool}")
    print(f"    Integration: {s.integration}")
    print(f"    Output: {s.output}")
    print(f"    Alert: {s.alert}")

เคล็ดลับ

• QLoRA: ใช้ QLoRA 4-bit สำหรับ GPU ที่มี VRAM จำกัด ผลใกล้เคียง LoRA
• Dataset: คุณภาพ Dataset สำคัญกว่าจำนวน 500 ตัวอย่างดีๆ ดีกว่า 5000 แย่ๆ
• RAG+LoRA: ใช้ RAG ร่วมกับ LoRA ดีที่สุด RAG ให้ Factual LoRA ให้ Format
• Privacy: รัน Self-hosted Compliance Data ไม่ส่งออกนอกองค์กร
• Review: AI ช่วย Draft แต่ GRC Team ต้อง Review ทุกครั้ง

LoRA คืออะไร

Low-Rank Adaptation Fine-tuning LLM Low-rank Matrix VRAM 8-24GB Train เร็ว Adapter 10-100MB QLoRA 4-bit Parameter 0.1% ของ Model

สรุป

LLM Fine-tuning LoRA QLoRA Compliance Automation PCI-DSS SOC2 GDPR Policy Check Report Generation vLLM Self-hosted Dataset Pipeline Production