Midjourney Prompt Audit Trail Logging — ติดตาม

Midjourney Audit Trail

Midjourney Prompt Audit Trail Logging AI Image Generation Compliance Governance Cost Tracking Usage Analytics Structured Logging Elasticsearch Dashboard

เนื้อหาเกี่ยวข้อง — อ่านต่อ: Radix UI Primitives Agile Scrum Kanban

Logging Architecture

# === AI Prompt Logging System ===

# Architecture:
# User -> Proxy API -> AI Service (Midjourney/DALL-E)
#                   -> Logger -> Elasticsearch
#                   -> Cost Tracker -> Database
#                   -> Policy Engine -> Filter/Block

# Python — Prompt Logger
# import json
# import datetime
# from elasticsearch import Elasticsearch
#
# es = Elasticsearch("http://elasticsearch:9200")
#
# class PromptLogger:
#     def __init__(self):
#         self.es = es
#
#     def log_prompt(self, user_id, prompt, params, result):
#         doc = {
#             "timestamp": datetime.datetime.utcnow().isoformat(),
#             "user_id": user_id,
#             "prompt": prompt,
#             "parameters": params,
#             "result_url": result.get("url"),
#             "model": params.get("model", "midjourney-v6"),
#             "cost_usd": self.calculate_cost(params),
#             "status": result.get("status", "success"),
#             "tokens_used": result.get("tokens", 0),
#             "generation_time_ms": result.get("time_ms", 0),
#             "content_flags": self.check_content(prompt),
#         }
#         self.es.index(index="ai-prompts", document=doc)
#         return doc
#
#     def calculate_cost(self, params):
#         base = 0.04  # per image
#         if params.get("quality") == "hd": base = 0.08
#         count = params.get("n", 1)
#         return base * count
#
#     def check_content(self, prompt):
#         blocked_terms = ["violence", "explicit", "harmful"]
#         flags = [t for t in blocked_terms if t in prompt.lower()]
#         return flags

from dataclasses import dataclass
from typing import List

@dataclass
class PromptLog:
    user: str
    prompt: str
    model: str
    cost: float
    status: str
    flags: int
    time_ms: int

logs = [
    PromptLog("alice", "futuristic city skyline, cyberpunk style", "midjourney-v6", 0.04, "success", 0, 12500),
    PromptLog("bob", "product photo, white background, minimal", "dall-e-3", 0.08, "success", 0, 8200),
    PromptLog("carol", "logo design, modern tech company", "midjourney-v6", 0.04, "success", 0, 15300),
    PromptLog("dave", "abstract art, colorful geometric", "stable-diffusion", 0.01, "success", 0, 5400),
    PromptLog("eve", "blocked content attempt", "midjourney-v6", 0.00, "blocked", 1, 50),
]

print("=== Prompt Audit Log ===")
for l in logs:
    flag_str = "FLAGGED" if l.flags > 0 else "OK"
    print(f"  [{l.status}] {l.user} — {l.model} ({flag_str})")
    print(f"    Prompt: {l.prompt[:50]}... | Cost:  | Time: {l.time_ms}ms")

Policy Engine

# === AI Usage Policy Engine ===

# Policy Configuration
# policies:
#   content_filter:
#     blocked_categories: [violence, explicit, hate_speech, deepfake]
#     action: block_and_alert
#   cost_limits:
#     per_user_daily: 5.00  # USD
#     per_team_monthly: 500.00
#     action: warn_at_80_block_at_100
#   approval_required:
#     for_external_use: true
#     for_marketing: true
#     approvers: [manager, legal]
#   retention:
#     prompts: 365  # days
#     images: 180
#     logs: 730

@dataclass
class PolicyRule:
    name: str
    type: str
    threshold: str
    action: str
    violations_30d: int

policies = [
    PolicyRule("Content Filter", "Block", "Blocked categories", "Block + Alert Admin", 3),
    PolicyRule("Daily Cost Limit", "Budget", "$5/user/day", "Warn 80%, Block 100%", 12),
    PolicyRule("Monthly Team Budget", "Budget", "$500/team/month", "Alert Manager", 2),
    PolicyRule("External Use Approval", "Workflow", "Marketing/External", "Require Approval", 8),
    PolicyRule("Prompt Length", "Limit", "Max 500 chars", "Truncate + Warn", 5),
    PolicyRule("Rate Limit", "Throttle", "20 images/hour", "Queue + Wait", 15),
]

print("\n=== Policy Rules ===")
for p in policies:
    print(f"  [{p.type}] {p.name}")
    print(f"    Threshold: {p.threshold} | Action: {p.action} | Violations: {p.violations_30d}")

# Cost Dashboard
cost_data = {
    "Total Spend (30d)": "$1,245",
    "Images Generated": "8,500",
    "Avg Cost/Image": "$0.15",
    "Top User": "alice ($180)",
    "Top Team": "Marketing ($450)",
    "Blocked Prompts": "15 (0.18%)",
    "Approval Pending": "3",
    "Budget Remaining": "$755 (60.6%)",
}

print(f"\nCost Dashboard:")
for k, v in cost_data.items():
    print(f"  {k}: {v}")

Compliance และ Dashboard

# === Compliance Dashboard ===

# Grafana Dashboard Queries (Elasticsearch)
# Total prompts: count(ai-prompts) where timestamp > now-30d
# Blocked rate: count(status=blocked) / count(*) * 100
# Cost by team: sum(cost_usd) group by team
# Top models: count(*) group by model
# Hourly usage: count(*) group by date_histogram(1h)

# Content Provenance — C2PA Standard
# C2PA (Coalition for Content Provenance and Authenticity)
# Embed metadata in generated images:
# - Generator: Midjourney v6 / DALL-E 3
# - Prompt: (hashed or full)
# - Timestamp
# - User/Organization
# - Digital Signature

# Kibana Saved Searches
# - All blocked prompts (last 7 days)
# - Cost anomalies (> 2x average)
# - New users first generation
# - Failed generations
# - Policy violations by user

compliance = {
    "EU AI Act": "Transparency ต้องระบุว่าสร้างด้วย AI",
    "Copyright": "ไม่ใช้ Prompt ที่ละเมิดลิขสิทธิ์ artist name",
    "Data Privacy": "ไม่ใส่ PII ใน Prompt เช่น ชื่อจริง ใบหน้า",
    "Content Safety": "กรอง Content ที่ไม่เหมาะสม อัตโนมัติ",
    "Audit Trail": "เก็บ Log ทุกการใช้งาน 2 ปี ขั้นต่ำ",
    "Access Control": "RBAC จำกัดสิทธิ์ตาม Role",
    "C2PA Metadata": "ฝัง Provenance ข้อมูลในภาพที่สร้าง",
}

print("Compliance Requirements:")
for req, desc in compliance.items():
    print(f"  [{req}]: {desc}")

# Implementation Checklist
checklist = [
    "ตั้ง Proxy API หน้า AI Service ทุกตัว",
    "Log ทุก Request/Response เข้า Elasticsearch",
    "สร้าง Content Filter ด้วย Keyword + ML Model",
    "ตั้ง Budget Limit per User/Team",
    "สร้าง Grafana Dashboard สำหรับ Usage + Cost",
    "ตั้ง Alert เมื่อ Policy Violation",
    "กำหนด Retention Policy ตาม Compliance",
    "ฝัง C2PA Metadata ในภาพที่สร้าง",
]

print(f"\n\nImplementation Checklist:")
for i, item in enumerate(checklist, 1):
    print(f"  {i}. {item}")

เคล็ดลับ

• Proxy: ตั้ง Proxy API หน้า AI Service ทุกตัว Log ได้ครบ
• Filter: กรอง Prompt อัตโนมัติ ก่อนส่งไป AI
• Budget: ตั้ง Budget Limit ป้องกันค่าใช้จ่ายบานปลาย
• C2PA: ฝัง Provenance Metadata ในภาพทุกรูป
• Review: ตรวจสอบ Audit Log เป็นประจำ ทุกสัปดาห์

Midjourney Audit Trail คืออะไร

บันทึกทุกการใช้ AI Image Prompt ผู้ใช้ เวลา ผลลัพธ์ Parameters Compliance Governance Cost Usage Pattern Legal

แนะนำเพิ่มเติม — เรียนเทรดกับ iCafeForex

เนื้อหาเกี่ยวข้อง — แนะนำให้อ่าน Register MQL5 — คู่มือเทรด Forex ฉบับสมบูรณ์ 2026

เนื้อหาเกี่ยวข้อง — อ่านต่อ: SQLite Litestream Technical Debt Management