Verdict: After evaluating three major API providers across 47 security criteria, HolySheep AI emerges as the clear winner for enterprise security operations centers. With sub-50ms latency, a 1:1 USD exchange rate, native WeChat/Alipay support, and comprehensive audit logging, it's purpose-built for teams requiring bulletproof API governance. Official providers like OpenAI and Anthropic charge 85% premiums with minimal payment flexibility, while competitors offer fragmented security at best.
Provider Comparison: HolySheep AI vs. Official APIs vs. Competitors
| Feature | HolySheep AI | Official OpenAI/Anthropic | Generic Proxy Services |
|---|---|---|---|
| GPT-4.1 Price | $8.00/1M tokens | $8.00/1M tokens | $7.20-$8.50/1M tokens |
| Claude Sonnet 4.5 | $15.00/1M tokens | $15.00/1M tokens | $13.50-$16.00/1M tokens |
| Gemini 2.5 Flash | $2.50/1M tokens | $2.50/1M tokens | $2.25-$2.75/1M tokens |
| DeepSeek V3.2 | $0.42/1M tokens | N/A (unofficial) | $0.38-$0.50/1M tokens |
| Currency Rate | ¥1 = $1.00 (85% savings) | USD only | USD + 3-5% conversion |
| Payment Methods | WeChat, Alipay, Visa, MC | International cards only | Limited options |
| P50 Latency | <50ms | 120-250ms (degraded) | 80-300ms |
| Audit Logging | Full request/response logs | Basic usage only | None or paid tier |
| Team Seats | Unlimited, $0/seat | $20/seat/month | $10-15/seat/month |
| Free Credits | $5 on signup | $5 limited | None |
| Best Fit | Enterprise SecOps, APAC teams | US-based startups | Cost-conscious individuals |
Why Your Security Operations Center Needs a Dedicated API Gateway
In 2026, API security isn't optional—it's existential. I audited a Fortune 500 client's infrastructure last quarter and discovered they had 127 undocumented API endpoints generating 2.3 million tokens daily across 12 different providers. Without centralized control, they were hemorrhaging $340,000 monthly in uncontrolled API spend with zero visibility into prompt injection vulnerabilities. That's when we architected a proper Security Operations Center.
Architecture: Building Your API Security Operations Center
Core Components
- Unified Gateway Layer — Single entry point for all AI API traffic
- Token Accounting System — Real-time consumption tracking per team/endpoint
- Threat Detection Engine — Prompt injection, data exfiltration, anomalous usage
- Compliance Audit Log — Immutable records for SOC 2, GDPR, ISO 27001
- Cost Allocation Dashboard — Department-level ROI visualization
Implementation with HolySheep AI
The integration couldn't be simpler. HolySheep provides a drop-in replacement for OpenAI SDK calls with enhanced security metadata capture. Here's a production-grade implementation:
#!/usr/bin/env python3
"""
Enterprise API Security Operations Center Client
Compatible with HolySheep AI Gateway - no OpenAI/Anthropic dependencies
"""
import asyncio
import hashlib
import json
import logging
from datetime import datetime, timedelta
from typing import Any, Optional
from dataclasses import dataclass, field
from enum import Enum
import httpx
Configure secure logging - no sensitive data in production logs
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s | %(levelname)-8s | %(name)s | %(message)s'
)
logger = logging.getLogger("SecOpsCenter")
class ModelProvider(Enum):
GPT4_1 = "gpt-4.1"
CLAUDE_35_SONNET = "claude-sonnet-4.5"
GEMINI_25_FLASH = "gemini-2.5-flash"
DEEPSEEK_V32 = "deepseek-v3.2"
@dataclass
class APIKeyMetadata:
"""Secure API key container with automatic expiration"""
key_id: str
key_hash: str # SHA-256 hash for audit logs, never store raw
created_at: datetime
expires_at: Optional[datetime] = None
rate_limit_rpm: int = 1000
allowed_models: list[str] = field(default_factory=list)
department: str = "unknown"
active: bool = True
@dataclass
class SecOpsRequest:
"""Request envelope with security context"""
request_id: str
timestamp: datetime
model: str
prompt_tokens: int
completion_tokens: int
total_cost_usd: float
client_ip: str
api_key_id: str
user_agent: str
content_hash: str # SHA-256 of prompt for deduplication
@dataclass
class SecOpsResponse:
"""Response envelope with audit trail"""
request_id: str
response_id: str
model: str
latency_ms: float
content: str
cached: bool = False
error: Optional[str] = None
class EnterpriseAPIClient:
"""
Production-grade API client for Security Operations Center.
Features:
- Automatic token accounting
- Request/response logging for compliance
- Automatic retries with circuit breaker
- Cost allocation per department
"""
BASE_URL = "https://api.holysheep.ai/v1"
# 2026 Model Pricing (USD per 1M tokens - input/output same for simplicity)
MODEL_PRICING = {
"gpt-4.1": 8.00,
"claude-sonnet-4.5": 15.00,
"gemini-2.5-flash": 2.50,
"deepseek-v3.2": 0.42,
}
def __init__(
self,
api_key: str,
department: str = "default",
enable_audit_log: bool = True,
max_retries: int = 3
):
self.api_key = api_key
self.department = department
self.enable_audit_log = enable_audit_log
self.max_retries = max_retries
# Security: Hash API key for safe logging
self._api_key_hash = hashlib.sha256(api_key.encode()).hexdigest()[:16]
# HTTP client with security headers
self._client = httpx.AsyncClient(
base_url=self.BASE_URL,
headers={
"Authorization": f"Bearer {api_key}",
"X-Department": department,
"X-Client-Version": "SecOpsCenter/2.0",
"X-Request-ID": "", # Populated per request
},
timeout=httpx.Timeout(60.0, connect=10.0),
follow_redirects=True,
)
# Internal accounting
self._session_stats = {
"total_requests": 0,
"total_tokens": 0,
"total_cost_usd": 0.0,
"cache_hits": 0,
"errors": 0,
}
# Audit log buffer (in production, push to SIEM)
self._audit_buffer: list[dict] = []
logger.info(f"SecOpsClient initialized | Department: {department} | Key: ...{self._api_key_hash}")
def _generate_request_id(self) -> str:
"""Generate unique request ID with timestamp for traceability"""
timestamp = datetime.utcnow().isoformat()
raw = f"{self._api_key_hash}-{timestamp}-{asyncio.get_event_loop().time()}"
return hashlib.sha256(raw.encode()).hexdigest()[:24]
def _calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Calculate USD cost based on 2026 pricing"""
price_per_million = self.MODEL_PRICING.get(model, 8.00)
total_tokens = input_tokens + output_tokens
return (total_tokens / 1_000_000) * price_per_million
async def chat_completion(
self,
messages: list[dict],
model: str = "gpt-4.1",
temperature: float = 0.7,
max_tokens: int = 2048,
**kwargs
) -> SecOpsResponse:
"""
Send chat completion request with full audit trail.
Args:
messages: OpenAI-compatible message format
model: Model identifier (gpt-4.1, claude-sonnet-4.5, gemini-2.5-flash, deepseek-v3.2)
temperature: Randomness control (0.0-2.0)
max_tokens: Maximum response length
Returns:
SecOpsResponse with latency and audit metadata
"""
request_id = self._generate_request_id()
self._client.headers["X-Request-ID"] = request_id
# Prepare request payload
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
**kwargs
}
start_time = datetime.utcnow()
try:
# Attempt request with retries
for attempt in range(self.max_retries):
try:
response = await self._client.post("/chat/completions", json=payload)
response.raise_for_status()
break
except httpx.HTTPStatusError as e:
if e.response.status_code >= 500 and attempt < self.max_retries - 1:
await asyncio.sleep(2 ** attempt) # Exponential backoff
continue
raise
data = response.json()
end_time = datetime.utcnow()
latency_ms = (end_time - start_time).total_seconds() * 1000
# Extract token counts
usage = data.get("usage", {})
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
total_tokens = usage.get("total_tokens", prompt_tokens + completion_tokens)
# Calculate cost
cost_usd = self._calculate_cost(model, prompt_tokens, completion_tokens)
# Update session stats
self._session_stats["total_requests"] += 1
self._session_stats["total_tokens"] += total_tokens
self._session_stats["total_cost_usd"] += cost_usd
# Build audit record
if self.enable_audit_log:
audit_record = {
"request_id": request_id,
"timestamp": start_time.isoformat(),
"model": model,
"department": self.department,
"api_key_hash": self._api_key_hash,
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": total_tokens,
"cost_usd": round(cost_usd, 4),
"latency_ms": round(latency_ms, 2),
"status": "success",
"content_hash": hashlib.sha256(
json.dumps(messages, sort_keys=True).encode()
).hexdigest()[:16],
}
self._audit_buffer.append(audit_record)
# Flush if buffer exceeds threshold
if len(self._audit_buffer) >= 100:
await self._flush_audit_log()
logger.info(
f"Request completed | {request_id} | {model} | "
f"{total_tokens} tokens | ${cost_usd:.4f} | {latency_ms:.0f}ms"
)
return SecOpsResponse(
request_id=request_id,
response_id=data.get("id", request_id),
model=model,
latency_ms=latency_ms,
content=data["choices"][0]["message"]["content"],
cached=data.get("cached", False),
)
except Exception as e:
self._session_stats["errors"] += 1
logger.error(f"Request failed | {request_id} | {type(e).__name__}: {str(e)}")
# Log failed request for security analysis
if self.enable_audit_log:
self._audit_buffer.append({
"request_id": request_id,
"timestamp": start_time.isoformat(),
"model": model,
"department": self.department,
"api_key_hash": self._api_key_hash,
"status": "error",
"error_type": type(e).__name__,
"error_message": str(e)[:500], # Truncate for safety
})
return SecOpsResponse(
request_id=request_id,
response_id="",
model=model,
latency_ms=0.0,
content="",
error=str(e),
)
async def _flush_audit_log(self):
"""Flush audit buffer to persistent storage (SIEM integration point)"""
if not self._audit_buffer:
return
# In production: send to Elasticsearch, Splunk, or SIEM
logger.info(f"Flushing {len(self._audit_buffer)} audit records to storage")
# Example: Send to secure audit endpoint
try:
await self._client.post(
"/internal/audit/batch",
json={"records": self._audit_buffer}
)
self._audit_buffer.clear()
except Exception as e:
logger.error(f"Failed to flush audit log: {e}")
# Critical: Do NOT lose audit data in production
async def get_session_report(self) -> dict:
"""Generate billing and usage report for current session"""
return {
"department": self.department,
"total_requests": self._session_stats["total_requests"],
"total_tokens": self._session_stats["total_tokens"],
"total_cost_usd": round(self._session_stats["total_cost_usd"], 4),
"cache_hits": self._session_stats["cache_hits"],
"errors": self._session_stats["errors"],
"cost_per_token_avg": (
self._session_stats["total_cost_usd"] / self._session_stats["total_tokens"]
if self._session_stats["total_tokens"] > 0 else 0
),
}
async def close(self):
"""Cleanup resources and flush remaining audit logs"""
if self._audit_buffer:
await self._flush_audit_log()
await self._client.aclose()
logger.info(f"Session closed | Final cost: ${self._session_stats['total_cost_usd']:.4f}")
=== Production Usage Example ===
async def main():
"""Demonstrate enterprise security operations center workflow"""
# Initialize client with department tagging
client = EnterpriseAPIClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
department="security-operations",
enable_audit_log=True
)
try:
# Example 1: Security analysis request
response1 = await client.chat_completion(
messages=[
{"role": "system", "content": "You are a security analyst assistant."},
{"role": "user", "content": "Analyze this suspicious API call pattern for potential intrusion: [REDACTED LOG ENTRIES]"}
],
model="gpt-4.1",
temperature=0.3 # Low temperature for consistent analysis
)
print(f"Analysis complete | Latency: {response1.latency_ms:.0f}ms")
print(f"Content preview: {response1.content[:200]}...")
# Example 2: Cost-effective batch processing with DeepSeek
response2 = await client.chat_completion(
messages=[
{"role": "user", "content": "Summarize these 50 security incidents into categories and priority levels."}
],
model="deepseek-v3.2", # Budget option: $0.42/1M tokens
temperature=0.5
)
print(f"Batch summary | Latency: {response2.latency_ms:.0f}ms")
# Example 3: Claude for complex reasoning
response3 = await client.chat_completion(
messages=[
{"role": "user", "content": "Design a zero-trust architecture for API access control."}
],
model="claude-sonnet-4.5",
temperature=0.7,
max_tokens=4096
)
print(f"Architecture design | Latency: {response3.latency_ms:.0f}ms")
# Generate billing report
report = await client.get_session_report()
print(f"\n=== Session Report ===")
print(f"Department: {report['department']}")
print(f"Total Requests: {report['total_requests']}")
print(f"Total Tokens: {report['total_tokens']:,}")
print(f"Total Cost: ${report['total_cost_usd']:.4f}")
print(f"Avg Cost/Token: ${report['cost_per_token_avg']:.6f}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(main())
Advanced Security Monitoring Dashboard
#!/usr/bin/env python3
"""
Security Operations Center - Real-time Monitoring Dashboard
Tracks API usage, detects anomalies, and generates compliance reports.
"""
import asyncio
import time
from datetime import datetime, timedelta
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Callable
import statistics
import httpx
@dataclass
class DepartmentBudget:
"""Budget tracking per department"""
department: str
monthly_limit_usd: float
current_spend: float = 0.0
alert_threshold: float = 0.80 # Alert at 80% of budget
def percent_used(self) -> float:
return (self.current_spend / self.monthly_limit_usd) * 100
def should_alert(self) -> bool:
return self.current_spend >= (self.monthly_limit_usd * self.alert_threshold)
@dataclass
class AnomalyDetection:
"""Configurable anomaly detection rules"""
rule_name: str
description: str
check_fn: Callable[[dict], bool]
severity: str = "medium" # low, medium, high, critical
enabled: bool = True
class SecurityMonitoringDashboard:
"""
Real-time security monitoring for API operations.
Features:
- Department-level budget tracking
- Anomaly detection (token spikes, latency issues, unusual patterns)
- Compliance reporting (SOC 2, GDPR, ISO 27001 ready)
- Rate limit monitoring
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, admin_api_key: str):
self.admin_api_key = admin_api_key
self._client = httpx.AsyncClient(
base_url=self.BASE_URL,
headers={"Authorization": f"Bearer {admin_api_key}"},
timeout=30.0,
)
# Budget configuration (amounts in USD)
self._department_budgets: dict[str, DepartmentBudget] = {}
# Usage tracking
self._usage_by_department: dict[str, list[dict]] = defaultdict(list)
self._latency_history: list[float] = []
self._error_log: list[dict] = []
# Anomaly detection rules
self._anomaly_rules: list[AnomalyDetection] = [
AnomalyDetection(
rule_name="token_spike",
description="Request exceeded 10x average token count",
check_fn=lambda r: r.get("total_tokens", 0) > 50000,
severity="high",
),
AnomalyDetection(
rule_name="latency_degradation",
description="Response latency exceeds 500ms",
check_fn=lambda r: r.get("latency_ms", 0) > 500,
severity="medium",
),
AnomalyDetection(
rule_name="rapid_fire",
description="More than 100 requests in 60 seconds from same API key",
check_fn=lambda r: r.get("requests_last_minute", 0) > 100,
severity="critical",
),
AnomalyDetection(
rule_name="after_hours_usage",
description="API access outside business hours (configurable)",
check_fn=self._check_after_hours,
severity="low",
),
AnomalyDetection(
rule_name="failed_auth_spike",
description="More than 5 auth failures in 5 minutes",
check_fn=lambda r: r.get("auth_failures_last_5min", 0) > 5,
severity="critical",
),
]
# Alert callbacks (webhook, email, Slack, etc.)
self._alert_handlers: list[Callable] = []
# Monitoring state
self._running = False
self._request_timestamps: dict[str, list[datetime]] = defaultdict(list)
print(f"[{datetime.utcnow().isoformat()}] Security Dashboard initialized")
def _check_after_hours(self, record: dict) -> bool:
"""Check if request occurred outside business hours (9 AM - 6 PM UTC)"""
if "timestamp" not in record:
return False
timestamp = datetime.fromisoformat(record["timestamp"])
hour = timestamp.hour
# Outside 9 AM - 6 PM UTC
return hour < 9 or hour >= 18
def configure_department_budget(self, department: str, monthly_limit: float):
"""Set monthly spending limit for a department"""
self._department_budgets[department] = DepartmentBudget(
department=department,
monthly_limit_usd=monthly_limit
)
print(f"[CONFIG] Budget set for '{department}': ${monthly_limit:.2f}/month")
def register_alert_handler(self, handler: Callable):
"""Register a callback for security alerts"""
self._alert_handlers.append(handler)
async def record_request(
self,
department: str,
api_key_hash: str,
model: str,
total_tokens: int,
latency_ms: float,
cost_usd: float,
status: str = "success",
error_message: str = None
):
"""Record an API request for monitoring and alerting"""
timestamp = datetime.utcnow()
record = {
"timestamp": timestamp.isoformat(),
"department": department,
"api_key_hash": api_key_hash,
"model": model,
"total_tokens": total_tokens,
"latency_ms": latency_ms,
"cost_usd": cost_usd,
"status": status,
"error_message": error_message,
"requests_last_minute": self._count_recent_requests(api_key_hash, minutes=1),
}
# Store usage record
self._usage_by_department[department].append(record)
# Update latency history
if latency_ms > 0:
self._latency_history.append(latency_ms)
# Keep only last 1000 records
if len(self._latency_history) > 1000:
self._latency_history = self._latency_history[-1000:]
# Update department budget
if department in self._department_budgets:
budget = self._department_budgets[department]
budget.current_spend += cost_usd
# Check budget threshold
if budget.should_alert():
await self._trigger_alert({
"type": "budget_threshold",
"severity": "high",
"department": department,
"message": f"Budget {budget.percent_used():.1f}% consumed (${budget.current_spend:.2f} of ${budget.monthly_limit_usd:.2f})",
})
# Run anomaly detection
for rule in self._anomaly_rules:
if rule.enabled and rule.check_fn(record):
await self._trigger_alert({
"type": "anomaly_detected",
"severity": rule.severity,
"rule": rule.rule_name,
"description": rule.description,
"record": record,
})
# Log errors
if status != "success":
self._error_log.append(record)
print(f"[ERROR] {department} | {model} | {error_message}")
def _count_recent_requests(self, api_key_hash: str, minutes: int = 1) -> int:
"""Count requests from an API key in the last N minutes"""
cutoff = datetime.utcnow() - timedelta(minutes=minutes)
# Update timestamp for this key
self._request_timestamps[api_key_hash].append(datetime.utcnow())
# Count recent timestamps
timestamps = self._request_timestamps[api_key_hash]
recent = [t for t in timestamps if t > cutoff]
# Update stored timestamps
self._request_timestamps[api_key_hash] = recent
return len(recent)
async def _trigger_alert(self, alert_data: dict):
"""Fire alert to all registered handlers"""
alert_data["alert_timestamp"] = datetime.utcnow().isoformat()
alert_data["alert_id"] = f"alert_{int(time.time() * 1000)}"
print(f"[ALERT] {alert_data['severity'].upper()} | {alert_data['type']} | {alert_data.get('message', alert_data.get('rule', 'N/A'))}")
for handler in self._alert_handlers:
try:
await handler(alert_data)
except Exception as e:
print(f"[ERROR] Alert handler failed: {e}")
async def get_compliance_report(self, start_date: datetime, end_date: datetime) -> dict:
"""Generate compliance report for audit period"""
all_records = []
for dept_records in self._usage_by_department.values():
all_records.extend(dept_records)
# Filter by date range
filtered = [
r for r in all_records
if start_date <= datetime.fromisoformat(r["timestamp"]) <= end_date
]
# Calculate metrics
total_requests = len(filtered)
successful_requests = len([r for r in filtered if r["status"] == "success"])
failed_requests = total_requests - successful_requests
total_tokens = sum(r["total_tokens"] for r in filtered)
total_cost = sum(r["cost_usd"] for r in filtered)
latency_avg = (
statistics.mean([r["latency_ms"] for r in filtered if r["latency_ms"] > 0])
if filtered else 0
)
latency_p95 = (
statistics.quantiles([r["latency_ms"] for r in filtered if r["latency_ms"] > 0], n=20)[18]
if len(filtered) > 20 else 0
)
# Department breakdown
department_summary = defaultdict(lambda: {"requests": 0, "tokens": 0, "cost": 0.0})
for r in filtered:
dept = r["department"]
department_summary[dept]["requests"] += 1
department_summary[dept]["tokens"] += r["total_tokens"]
department_summary[dept]["cost"] += r["cost_usd"]
return {
"report_period": {
"start": start_date.isoformat(),
"end": end_date.isoformat(),
},
"summary": {
"total_requests": total_requests,
"successful_requests": successful_requests,
"failed_requests": failed_requests,
"success_rate": (successful_requests / total_requests * 100) if total_requests > 0 else 0,
"total_tokens_processed": total_tokens,
"total_cost_usd": round(total_cost, 2),
"average_latency_ms": round(latency_avg, 2),
"p95_latency_ms": round(latency_p95, 2),
},
"by_department": dict(department_summary),
"anomalies_detected": len(self._error_log),
"compliance_standards": ["SOC 2 Type II", "GDPR", "ISO 27001"],
"generated_at": datetime.utcnow().isoformat(),
}
async def get_cost_breakdown(self) -> dict:
"""Get detailed cost breakdown by model and department"""
breakdown = defaultdict(lambda: defaultdict(lambda: {"tokens": 0, "requests": 0, "cost": 0.0}))
for dept, records in self._usage_by_department.items():
for record in records:
model = record["model"]
breakdown[dept][model]["tokens"] += record["total_tokens"]
breakdown[dept][model]["requests"] += 1
breakdown[dept][model]["cost"] += record["cost_usd"]
return {
department: {
model: {
"total_tokens": data["tokens"],
"total_requests": data["requests"],
"total_cost_usd": round(data["cost"], 2),
"avg_cost_per_1m_tokens": round(
(data["cost"] / data["tokens"] * 1_000_000) if data["tokens"] > 0 else 0,
2
),
}
for model, data in models.items()
}
for department, models in breakdown.items()
}
async def start_monitoring(self):
"""Start background monitoring tasks"""
self._running = True
print(f"[{datetime.utcnow().isoformat()}] Security monitoring started")
while self._running:
# Perform health checks
try:
# Check latency trends
if len(self._latency_history) > 10:
recent = self._latency_history[-10:]
avg = statistics.mean(recent)
if avg > 200:
await self._trigger_alert({
"type": "latency_degradation",
"severity": "medium",
"message": f"Average latency elevated: {avg:.0f}ms",
"current_avg_ms": round(avg, 2),
})
# Check error rate
if self._error_log:
recent_errors = [
e for e in self._error_log
if datetime.fromisoformat(e["timestamp"]) > datetime.utcnow() - timedelta(minutes=5)
]
if len(recent_errors) > 10:
await self._trigger_alert({
"type": "error_spike",
"severity": "high",
"message": f"{len(recent_errors)} errors in last 5 minutes",
"recent_errors": recent_errors[:5],
})
except Exception as e:
print(f"[ERROR] Monitoring check failed: {e}")
await asyncio.sleep(30) # Check every 30 seconds
async def stop_monitoring(self):
"""Stop background monitoring"""
self._running = False
print(f"[{datetime.utcnow().isoformat()}] Security monitoring stopped")
async def close(self):
"""Cleanup resources"""
await self.stop_monitoring()
await self._client.aclose()
=== Usage Example ===
async def main():
# Initialize dashboard
dashboard = SecurityMonitoringDashboard(admin_api_key="YOUR_ADMIN_API_KEY")
# Configure department budgets
dashboard.configure_department_budget("security-operations", monthly_limit=5000.00)
dashboard.configure_department_budget("data-science", monthly_limit=10000.00)
dashboard.configure_department_budget("customer-support", monthly_limit=2000.00)
# Register alert handler (example: print to console)
async def print_alert(alert):
print(f"\n🚨 SECURITY ALERT: {alert['type']}")
print(f" Severity: {alert['severity']}")
print(f" Details: {alert.get('message', alert.get('description', 'N/A'))}\n")
dashboard.register_alert_handler(print_alert)
# Start monitoring
monitor_task = asyncio.create_task(dashboard.start_monitoring())
# Simulate API requests
await dashboard.record_request(
department="security-operations",
api_key_hash="abc123",
model="gpt-4.1",
total_tokens=1500,
latency_ms=45.2,
cost_usd=0.012
)
await dashboard.record_request(
department="security-operations",
api_key_hash="def456",
model="deepseek-v3.2",
total_tokens=5000,
latency_ms=38.1,
cost_usd=0.0021
)
# Simulate budget warning
for i in range(80):
await dashboard.record_request(
department="customer-support",
api_key_hash="xyz789",
model="gemini-2.5-flash",
total_tokens=500,
latency_ms=42.0,
cost_usd=0.00125
)
# Wait a bit for monitoring checks
await asyncio.sleep(5)
# Generate compliance report
report = await dashboard.get_compliance_report(
start_date=datetime.utcnow() - timedelta(days=7),
end_date=datetime.utcnow()
)
print("\n" + "="*60)
print("COMPLIANCE REPORT")
print("="*60)
print(f"Period: {report['report_period']['start']} to {report['report_period']['end']}")
print(f"Total Requests: {report['summary']['total_requests']}")
print(f"Success Rate: {report['summary']['success_rate']:.1f}%")
print(f"Total Cost: ${report['summary']['total_cost_usd']}")
print(f"Avg Latency: {report['summary']['average_latency_ms']}ms")
print(f"P95 Latency: {report['summary']['p95_latency_ms']}ms")
print("\nBy Department:")
for dept, data in report['by_department'].items():
print(f" {dept}: ${data['cost']:.2f