supabase
Handles the full Supabase workflow from schema changes to deployment, with built-in security guardrails that catch common traps like RLS...
klingai-audit-logging
Implement audit logging for Kling AI operations for compliance and security.
Full skill instructions
Compliance-grade audit logging for Kling AI API operations. Every task submission, status change, and credential usage is captured in tamper-evident structured logs.
import json
import hashlib
import time
from datetime import datetime
from pathlib import Path
class AuditLogger:
"""Append-only audit log with integrity checksums."""
def __init__(self, log_dir: str = "audit"):
self.log_dir = Path(log_dir)
self.log_dir.mkdir(exist_ok=True)
self._prev_hash = "genesis"
def _compute_hash(self, entry: dict) -> str:
raw = json.dumps(entry, sort_keys=True) + self._prev_hash
return hashlib.sha256(raw.encode()).hexdigest()[:16]
def log(self, event_type: str, actor: str, details: dict):
"""Write a tamper-evident audit entry."""
entry = {
"timestamp": datetime.utcnow().isoformat() + "Z",
"event_type": event_type,
"actor": actor,
"details": details,
"prev_hash": self._prev_hash,
}
entry["hash"] = self._compute_hash(entry)
self._prev_hash = entry["hash"]
date = datetime.utcnow().strftime("%Y-%m-%d")
filepath = self.log_dir / f"audit-{date}.jsonl"
with open(filepath, "a") as f:
f.write(json.dumps(entry) + "\n")
return entry["hash"]
class KlingAuditClient:
"""Kling client with full audit trail."""
def __init__(self, base_client, audit: AuditLogger, actor: str = "system"):
self.client = base_client
self.audit = audit
self.actor = actor
def text_to_video(self, prompt: str, **kwargs):
# Log submission
self.audit.log("task_submitted", self.actor, {
"action": "text_to_video",
"model": kwargs.get("model", "kling-v2-master"),
"duration": kwargs.get("duration", 5),
"mode": kwargs.get("mode", "standard"),
"prompt_hash": hashlib.sha256(prompt.encode()).hexdigest()[:16],
"prompt_length": len(prompt),
})
result = self.client.text_to_video(prompt, **kwargs)
# Log completion
self.audit.log("task_completed", self.actor, {
"action": "text_to_video",
"status": "succeed",
"video_count": len(result.get("videos", [])),
})
return result
def log_auth_event(self, event: str, success: bool):
self.audit.log("auth_event", self.actor, {
"event": event,
"success": success,
"access_key_prefix": self.client.config.access_key[:8] + "...",
})
def verify_audit_chain(log_file: str) -> bool:
"""Verify tamper-evidence of audit log chain."""
prev_hash = "genesis"
entries = []
with open(log_file) as f:
for line_num, line in enumerate(f, 1):
entry = json.loads(line)
entries.append(entry)
if entry["prev_hash"] != prev_hash:
print(f"Chain broken at line {line_num}: "
f"expected prev_hash={prev_hash}, got {entry['prev_hash']}")
return False
# Recompute hash
check_entry = {k: v for k, v in entry.items() if k != "hash"}
raw = json.dumps(check_entry, sort_keys=True) + prev_hash
expected_hash = hashlib.sha256(raw.encode()).hexdigest()[:16]
if entry["hash"] != expected_hash:
print(f"Hash mismatch at line {line_num}")
return False
prev_hash = entry["hash"]
print(f"Verified {len(entries)} entries -- chain intact")
return True
def generate_audit_report(log_dir: str = "audit", days: int = 30) -> dict:
"""Generate compliance audit report."""
from collections import Counter
from datetime import timedelta
log_path = Path(log_dir)
events = []
cutoff = datetime.utcnow() - timedelta(days=days)
for filepath in sorted(log_path.glob("audit-*.jsonl")):
with open(filepath) as f:
for line in f:
entry = json.loads(line)
if entry["timestamp"] >= cutoff.isoformat():
events.append(entry)
event_types = Counter(e["event_type"] for e in events)
actors = Counter(e["actor"] for e in events)
report = {
"period_days": days,
"total_events": len(events),
"event_types": dict(event_types),
"unique_actors": len(actors),
"actors": dict(actors),
"first_event": events[0]["timestamp"] if events else None,
"last_event": events[-1]["timestamp"] if events else None,
}
print(f"\n=== Audit Report ({days} days) ===")
print(f"Total events: {report['total_events']}")
for event_type, count in event_types.most_common():
print(f" {event_type}: {count}")
print(f"Actors: {', '.join(actors.keys())}")
return report
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