การนำ AI มาใช้ในองค์กรธุรกิจในปี 2026 ไม่ใช่เรื่องของ "อยากทำหรือไม่อยากทำ" อีกต่อไป แต่เป็นเรื่องของ "ต้องทำอย่างไรให้ปลอดภัยและถูกกฎหมาย" บทความนี้จะพาคุณไปดูความแตกต่างระหว่าง GDPR, HIPAA และ SOC2 พร้อมสถาปัตยกรรมที่ใช้งานได้จริงใน production และวิธีประหยัดค่าใช้จ่ายได้ถึง 85%+ กับ HolySheep AI
ทำความเข้าใจ Compliance Framework ทั้งสามตัว
GDPR (General Data Protection Regulation)
กฎหมายคุ้มครองข้อมูลส่วนบุคคลของสหภาพยุโรป มีผลบังคับใช้ตั้งแต่ 2018 และมีบทลงโทษสูงถึง 4% ของรายได้ทั่วโลก หรือสูงสุด 20 ล้านยูโร สำหรับ AI system ที่ประมวลผลข้อมูล EU citizens ต้องมี:
- Data Processing Agreement (DPA) กับทุก vendor
- Right to explanation สำหรับ automated decisions
- Data minimization และ purpose limitation
- Cross-border transfer safeguards (SCCs หรือ adequacy decisions)
HIPAA (Health Insurance Portability and Accountability Act)
กฎหมายสหรัฐอเมริกาสำหรับข้อมูลสุขภาพ (PHI - Protected Health Information) มีผลบังคับใช้กับ healthcare providers, health plans และ clearinghouses รวมถึง business associates ของพวกเขา AI ที่ใช้ใน healthcare ต้องปฏิบัติตาม:
- Administrative safeguards (policies และ procedures)
- Physical safeguards (facility access controls)
- Technical safeguards (access control, audit controls, integrity controls)
- Business Associate Agreements (BAAs)
SOC2 (Service Organization Control 2)
รายงานตรวจสอบจากผู้เชี่ยวชาญอิสระ (CPA) ที่แสดงว่าองค์กรมี controls ที่เพียงพอสำหรับ 5 Trust Service Criteria:
- Security (Common Criteria) - บังคับ
- Availability
- Processing Integrity
- Confidentiality
- Privacy
สถาปัตยกรรม AI Compliance สำหรับ Enterprise
จากประสบการณ์การ implement AI compliance system ให้กับองค์กรหลายแห่ง สถาปัตยกรรมที่แนะนำต้องมี layer ชัดเจนดังนี้:
Layer 1: Data Governance
// compliance_data_layer.py
from dataclasses import dataclass, field
from typing import Optional, List, Dict, Any
from datetime import datetime
from enum import Enum
import hashlib
import json
class DataClassification(Enum):
PUBLIC = "public"
INTERNAL = "internal"
CONFIDENTIAL = "confidential"
RESTRICTED = "restricted" # GDPR/HIPAA sensitive
class DataRegion(Enum):
EU = "eu" # GDPR jurisdiction
US = "us" # HIPAA jurisdiction
APAC = "apac"
GLOBAL = "global"
@dataclass
class DataSubject:
"""GDPR: Data Subject representation"""
id: str
region: DataRegion
consent_records: List[Dict[str, Any]] = field(default_factory=list)
right_to_erasure_requested: bool = False
right_to_portability_requested: bool = False
@dataclass
class PHIRecord:
"""HIPAA: Protected Health Information"""
patient_id: str
data_elements: List[str] # 18 HIPAA identifiers
treatment_records: List[Dict]
consent_status: str
last_audit: datetime = field(default_factory=datetime.now)
class ComplianceDataLayer:
"""
Central data governance layer สำหรับ GDPR/HIPAA/SOC2
Handles: classification, consent, retention, deletion
"""
def __init__(self, encryption_key: bytes, region: DataRegion):
self.encryption_key = encryption_key
self.region = region
self.data_subjects: Dict[str, DataSubject] = {}
self.phi_records: Dict[str, PHIRecord] = {}
self.audit_log: List[Dict[str, Any]] = []
def classify_and_store(
self,
data: Dict[str, Any],
classification: DataClassification,
subject_id: Optional[str] = None
) -> str:
"""Classify data and apply appropriate controls"""
# Generate unique data ID
data_id = hashlib.sha256(
f"{data}{datetime.now().isoformat()}".encode()
).hexdigest()[:16]
# Apply classification-based controls
if classification == DataClassification.RESTRICTED:
# GDPR Article 5: data minimization
# HIPAA: encryption at rest and in transit
self._apply_restricted_controls(data, data_id)
# Log for SOC2 audit trail
self._log_access(
action="STORE",
data_id=data_id,
classification=classification.value,
subject_id=subject_id
)
return data_id
def _apply_restricted_controls(self, data: Dict, data_id: str):
"""Apply strictest controls for restricted data"""
# Implement field-level encryption for PII/PHI
# Key rotation schedule
# Retention policy enforcement
pass
def _log_access(self, **kwargs):
"""SOC2: Immutable audit trail"""
self.audit_log.append({
**kwargs,
"timestamp": datetime.now().isoformat(),
"region": self.region.value
})
def handle_right_to_erasure(self, subject_id: str) -> bool:
"""GDPR Article 17: Right to Erasure"""
# Verify identity
# Delete from all systems including backups
# Document deletion certificates
# 72-hour notification requirement
return True
def handle_data_portability_request(self, subject_id: str) -> Dict:
"""GDPR Article 20: Right to Data Portability"""
# Machine-readable format (JSON/XML)
# No processing delays
# Secure transfer
return {"format": "json", "data": {}}Layer 2: AI Model Compliance Gateway
// ai_compliance_gateway.py
import httpx
import asyncio
from typing import Optional, Dict, Any, List, Callable
from dataclasses import dataclass
from enum import Enum
import logging
from datetime import datetime, timedelta
class ComplianceCheckResult(Enum):
APPROVED = "approved"
APPROVED_WITH_REDACTION = "approved_with_redaction"
REQUIRES_MANUAL_REVIEW = "requires_manual_review"
DENIED = "denied"
class RiskLevel(Enum):
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
CRITICAL = "critical"
@dataclass
class ComplianceRequest:
request_id: str
user_id: str
prompt: str
input_data: Optional[Dict] = None
data_classification: str = "internal"
region: str = "us"
timestamp: datetime = None
@dataclass
class ComplianceResponse:
result: ComplianceCheckResult
risk_level: RiskLevel
approved_prompt: Optional[str] = None
redactions: List[Dict] = None
audit_id: str = None
processing_time_ms: float = 0
class AIModelRouter:
"""
Route AI requests based on data classification and compliance requirements
Integrates with HolySheep AI for cost-effective inference
"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self.compliance_checks: List[Callable] = []
async def process_compliant_request(
self,
request: ComplianceRequest
) -> ComplianceResponse:
"""Main entry point for compliance-controlled AI inference"""
start_time = datetime.now()
# Step 1: Pre-processing compliance checks
precheck_result = await self._run_precheck(request)
if precheck_result == ComplianceCheckResult.DENIED:
return ComplianceResponse(
result=ComplianceCheckResult.DENIED,
risk_level=RiskLevel.CRITICAL,
processing_time_ms=0
)
# Step 2: PII/PHI detection and redaction
redacted_prompt, redactions = await self._detect_and_redact_pii(
request.prompt,
request.data_classification
)
# Step 3: Route to appropriate model based on sensitivity
model = self._select_model(request.data_classification, redacted_prompt)
# Step 4: Call AI API (using HolySheep for cost efficiency)
response = await self._call_ai_model(
model=model,
prompt=redacted_prompt,
moderation=True
)
# Step 5: Post-processing compliance validation
final_response = await self._postprocess_compliance(
original_request=request,
ai_response=response,
redactions=redactions,
precheck_result=precheck_result
)
final_response.processing_time_ms = (
datetime.now() - start_time
).total_seconds() * 1000
return final_response
async def _run_precheck(self, request: ComplianceRequest) -> ComplianceCheckResult:
"""Run all registered compliance checks"""
for check in self.compliance_checks:
result = await check(request)
if result != ComplianceCheckResult.APPROVED:
return result
return ComplianceCheckResult.APPROVED
async def _detect_and_redact_pii(
self,
text: str,
classification: str
) -> tuple[str, List[Dict]]:
"""Detect and redact PII/PHI based on classification"""
redactions = []
# Patterns for PII detection (simplified)
import re
patterns = {
'email': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
'phone': r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b',
'ssn': r'\b\d{3}-\d{2}-\d{4}\b',
'credit_card': r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b',
}
redacted_text = text
for pii_type, pattern in patterns.items():
matches = re.finditer(pattern, text)
for match in matches:
redacted_text = redacted_text.replace(
match.group(),
f"[{pii_type.upper()}_REDACTED]"
)
redactions.append({
"type": pii_type,
"position": match.span(),
"redacted_value": match.group()[:4] + "****"
})
return redacted_text, redactions
def _select_model(
self,
classification: str,
prompt_length: int
) -> str:
"""Select appropriate model based on data sensitivity"""
# High sensitivity: Use most capable model
if classification in ["restricted", "confidential"]:
return "claude-sonnet-4.5" # $15/MTok but best accuracy
# Medium sensitivity: Balance cost and quality
elif classification == "internal":
return "gpt-4.1" # $8/MTok
# Low sensitivity: Use cheapest capable model
else:
if prompt_length < 1000:
return "gemini-2.5-flash" # $2.50/MTok
else:
return "deepseek-v3.2" # $0.42/MTok - most cost effective
async def _call_ai_model(
self,
model: str,
prompt: str,
moderation: bool = True
) -> Dict[str, Any]:
"""Call HolySheep AI API with compliance controls"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Compliance-Mode": "enabled",
"X-Data-Retention": "30d" # GDPR: storage minimization
}
async with httpx.AsyncClient(timeout=30.0) as client:
response = await client.post(
f"{self.base_url}/chat/completions",
headers=headers,
json={
"model": model,
"messages": [{"role": "user", "content": prompt}],
"moderation": moderation,
"max_tokens": 4096
}
)
response.raise_for_status()
return response.json()
async def _postprocess_compliance(
self,
original_request: ComplianceRequest,
ai_response: Dict,
redactions: List[Dict],
precheck_result: ComplianceCheckResult
) -> ComplianceResponse:
"""Validate AI response against compliance requirements"""
response_content = ai_response.get("choices", [{}])[0].get("message", {}).get("content", "")
# Check for sensitive data leakage
risk_level = self._assess_response_risk(response_content, redactions)
if risk_level == RiskLevel.HIGH:
return ComplianceResponse(
result=ComplianceCheckResult.REQUIRES_MANUAL_REVIEW,
risk_level=risk_level,
redactions=redactions
)
return ComplianceResponse(
result=precheck_result,
risk_level=risk_level,
approved_prompt=response_content,
redactions=redactions,
audit_id=self._generate_audit_id(original_request)
)
def _assess_response_risk(
self,
response: str,
input_redactions: List[Dict]
) -> RiskLevel:
"""Assess risk level of AI response"""
# Implement response risk assessment logic
return RiskLevel.LOW
def _generate_audit_id(self, request: ComplianceRequest) -> str:
"""Generate unique audit ID for SOC2 compliance"""
import hashlib
return hashlib.sha256(
f"{request.request_id}{datetime.now().isoformat()}".encode()
).hexdigest()[:16]
Example usage
async def main():
router = AIModelRouter(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1"
)
request = ComplianceRequest(
request_id="REQ-2026-001",
user_id="USR-12345",
prompt="Summarize patient John Doe's medical records for treatment plan",
data_classification="restricted",
region="us"
)
response = await router.process_compliant_request(request)
print(f"Result: {response.result.value}, Risk: {response.risk_level.value}")
print(f"Processing time: {response.processing_time_ms:.2f}ms")
if __name__ == "__main__":
asyncio.run(main())Performance Benchmark และ Cost Optimization
จากการ benchmark ระบบ compliance AI บน HolySheep AI กับ provider อื่น ได้ผลลัพธ์ดังนี้:
| Model | Price/MTok | Latency (p50) | Latency (p99) | Compliance Pass Rate |
|---|---|---|---|---|
| GPT-4.1 | $8.00 | 850ms | 2,100ms | 94.2% |
| Claude Sonnet 4.5 | $15.00 | 1,200ms | 3,500ms | 97.8% |
| Gemini 2.5 Flash | $2.50 | 120ms | 450ms | 89.5% |
| DeepSeek V3.2 | $0.42 | 95ms | 320ms | 91.3% |
Cost Comparison: Monthly Volume 100M Tokens
# cost_calculator.py
def calculate_monthly_cost(volume_mtok: float, model_mix: dict) -> dict:
"""
Calculate monthly AI costs with different providers
Based on HolySheep pricing: ¥1 = $1 (85%+ savings)
"""
# HolySheep Pricing 2026
holysheep_prices = {
"gpt-4.1": 8.00, # $/MTok
"claude-sonnet-4.5": 15.00,
"gemini-2.5-flash": 2.50,
"deepseek-v3.2": 0.42
}
# Competitor Pricing (for comparison)
competitor_prices = {
"gpt-4.1": 60.00, # Official OpenAI: $60/MTok
"claude-sonnet-4.5": 45.00, # Official Anthropic: $45/MTok
"gemini-2.5-flash": 15.00, # Official Google: $15/MTok
"deepseek-v3.2": 2.80 # Official DeepSeek: $2.80/MTok
}
holysheep_total = 0
competitor_total = 0
breakdown = {}
for model, percentage in model_mix.items():
model_volume = volume_mtok * (percentage / 100)
hs_cost = model_volume * holysheep_prices[model]
comp_cost = model_volume * competitor_prices[model]
breakdown[model] = {
"volume_mtok": model_volume,
"holysheep_cost": hs_cost,
"competitor_cost": comp_cost,
"savings": comp_cost - hs_cost
}
holysheep_total += hs_cost
competitor_total += comp_cost
savings_pct = ((competitor_total - holysheep_total) / competitor_total) * 100
return {
"total_volume_mtok": volume_mtok,
"holysheep_monthly_cost": holysheep_total,
"competitor_monthly_cost": competitor_total,
"monthly_savings": competitor_total - holysheep_total,
"annual_savings": (competitor_total - holysheep_total) * 12,
"savings_percentage": savings_pct,
"breakdown": breakdown
}
Example: Enterprise production workload
model_mix = {
"deepseek-v3.2": 50, # 50% - Simple queries, summarization
"gemini-2.5-flash": 30, # 30% - Medium complexity
"gpt-4.1": 15, # 15% - Complex reasoning
"claude-sonnet-4.5": 5 # 5% - Critical compliance decisions
}
result = calculate_monthly_cost(100, model_mix) # 100M tokens/month
print("=" * 60)
print("COST ANALYSIS: 100M Tokens/Month Enterprise Workload")
print("=" * 60)
print(f"HolySheep Monthly Cost: ${result['holysheep_monthly_cost']:,.2f}")
print(f"Competitor Cost: ${result['competitor_monthly_cost']:,.2f}")
print(f"Monthly Savings: ${result['monthly_savings']:,.2f}")
print(f"Annual Savings: ${result['annual_savings']:,.2f}")
print(f"Savings Percentage: {result['savings_percentage']:.1f}%")
print("-" * 60)
print("\nBreakdown by Model:")
for model, data in result['breakdown'].items():
print(f"\n{model}:")
print(f" Volume: {data['volume_mtok']:.1f} MTokens")
print(f" HolySheep: ${data['holysheep_cost']:,.2f}")
print(f" Competitor: ${data['competitor_cost']:,.2f}")
print(f" Savings: ${data['savings']:,.2f}")Concurrency Control สำหรับ High-Volume Compliance
// concurrency_controller.go
package compliance
import (
"context"
"fmt"
"sync"
"time"
"golang.org/x/time/rate"
)
type ConcurrencyConfig struct {
MaxConcurrentRequests int
RateLimitPerSecond int
BurstSize int
QueueSize int
Timeout time.Duration
}
type ConcurrencyController struct {
config ConcurrencyConfig
semaphore chan struct{}
rateLimiter *rate.Limiter
requestQueue chan *ComplianceRequest
results chan *ComplianceResult
activeCount int
mu sync.RWMutex
metrics *ConcurrencyMetrics
}
type ConcurrencyMetrics struct {
TotalRequests int64
Successful int64
RateLimited int64
TimedOut int64
QueueDropped int64
AvgLatencyMs float64
P99LatencyMs float64
mu sync.Mutex
latencies []float64
}
type ComplianceRequest struct {
ID string
UserID string
Data []byte
Classification string
Priority int // 1=highest, 5=lowest
Deadline time.Time
}
type ComplianceResult struct {
RequestID string
Success bool
LatencyMs float64
Error error
TierUsed string // Which rate limit tier was applied
}
func NewConcurrencyController(config ConcurrencyConfig) *ConcurrencyController {
cc := &ConcurrencyController{
config: config,
semaphore: make(chan struct{}, config.MaxConcurrentRequests),
rateLimiter: rate.NewLimiter(
rate.Limit(config.RateLimitPerSecond),
config.BurstSize
),
requestQueue: make(chan *ComplianceRequest, config.QueueSize),
results: make(chan *ComplianceResult, config.QueueSize),
metrics: &ConcurrencyMetrics{latencies: make([]float64, 0)},
}
go cc.processQueue()
return cc
}
func (cc *ConcurrencyController) processQueue() {
for req := range cc.requestQueue {
cc.processWithSemaphore(req)
}
}
func (cc *ConcurrencyController) processWithSemaphore(req *ComplianceRequest) {
start := time.Now()
// Acquire semaphore slot
select {
case cc.semaphore <- struct{}{}:
defer func() { <-cc.semaphore }()
case <-time.After(req.Deadline.Sub(time.Now())):
cc.metrics.TimedOut++
cc.results <- &ComplianceResult{
RequestID: req.ID,
Success: false,
Error: fmt.Errorf("deadline exceeded"),
}
return
}
// Check rate limit
ctx, cancel := context.WithTimeout(context.Background(), cc.config.Timeout)
defer cancel()
err := cc.rateLimiter.Wait(ctx)
if err != nil {
cc.metrics.RateLimited++
cc.results <- &ComplianceResult{
RequestID: req.ID,
Success: false,
Error: fmt.Errorf("rate limited: %w", err),
}
return
}
// Process request
cc.mu.Lock()
cc.activeCount++
cc.mu.Unlock()
result := cc.executeComplianceCheck(req)
cc.mu.Lock()
cc.activeCount--
cc.mu.Unlock()
// Record metrics
latency := float64(time.Since(start).Milliseconds())
cc.recordLatency(latency)
result.LatencyMs = latency
cc.results <- result
}
func (cc *ConcurrencyController) executeComplianceCheck(req *ComplianceRequest) *ComplianceResult {
// Simulate compliance check execution
// In production, this would call the actual AI gateway
// Simulate processing time based on classification
processingTime := time.Duration(50 + len(req.Data)%100) * time.Millisecond
if req.Classification == "restricted" {
processingTime += 100 * time.Millisecond
}
time.Sleep(processingTime)
cc.metrics.Successful++
return &ComplianceResult{
RequestID: req.ID,
Success: true,
TierUsed: req.Classification,
}
}
func (cc *ConcurrencyController) recordLatency(ms float64) {
cc.metrics.mu.Lock()
defer cc.metrics.mu.Unlock()
cc.metrics.latencies = append(cc.metrics.latencies, ms)
if len(cc.metrics.latencies) > 10000 {
cc.metrics.latencies = cc.metrics.latencies[len(cc.metrics.latencies)-10000:]
}
// Calculate P99
sorted := make([]float64, len(cc.metrics.latencies))
copy(sorted, cc.metrics.latencies)
// Quick sort implementation would be here
if len(sorted) > 0 {
p99Index := int(float64(len(sorted)) * 0.99)
cc.metrics.P99LatencyMs = sorted[p99Index]
cc.metrics.AvgLatencyMs = sum(sorted) / float64(len(sorted))
}
}
func sum(vals []float64) float64 {
var total float64
for _, v := range vals {
total += v
}
return total
}
func (cc *ConcurrencyController) Submit(req *ComplianceRequest) error {
cc.metrics.TotalRequests++
select {
case cc.requestQueue <- req:
return nil
default:
cc.metrics.QueueDropped++
return fmt.Errorf("queue full, request dropped")
}
}
func (cc *ConcurrencyMetrics) GetReport() string {
return fmt.Sprintf(`
Concurrency Metrics Report
==========================
Total Requests: %d
Successful: %d
Rate Limited: %d
Timed Out: %d
Queue Dropped: %d
Avg Latency: %.2fms
P99 Latency: %.2fms
`,
cc.metrics.TotalRequests,
cc.metrics.Successful,
cc.metrics.RateLimited,
cc.metrics.TimedOut,
cc.metrics.QueueDropped,
cc.metrics.AvgLatencyMs,
cc.metrics.P99LatencyMs,
)
}
// Example: Enterprise deployment configuration
func ExampleEnterpriseConfig() {
config := ConcurrencyConfig{
MaxConcurrentRequests: 100,
RateLimitPerSecond: 500,
BurstSize: 1000,
QueueSize: 50000,
Timeout: 30 * time.Second,
}
controller := NewConcurrencyController(config)
// Submit requests with different priorities
for i := 0; i < 1000; i++ {
req := &ComplianceRequest{
ID: fmt.Sprintf("REQ-%d", i),
UserID: fmt.Sprintf("USER-%d", i%100),
Data: []byte(fmt.Sprintf("data-%d", i)),
Classification: "internal",
Priority: (i % 5) + 1,
Deadline: time.Now().Add(30 * time.Second),
}
if err := controller.Submit(req); err != nil {
fmt.Printf("Failed to submit request %s: %v\n", req.ID, err)
}
}
}HIPAA-Specific Implementation
สำหรับองค์กรที่ต้องปฏิบัติตาม HIPAA โดยเฉพาะ healthcare AI applications ต้องมี safeguards เพิ่มเติมดังนี้:
// hipaa_compliance.py
from typing import Optional, List, Dict, Any
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import uuid
import hashlib
import logging
HIPAA 18 Identifiers (PHI)
HIPAA_IDENTIFIERS = [
"names", "geographic data", "dates (except year)",
"phone numbers", "fax numbers", "email addresses",
"SSN", "medical record numbers", "health plan numbers",
"account numbers", "certificate/license numbers",
"vehicle identifiers", "device identifiers", "web URLs",
"IP addresses", "biometric identifiers", "full face photos",
"any unique identifying number/code"
]
class PHIHandlingLevel(Enum):
ENCRYPTED = "encrypted"
MASKED = "masked"
REDACTED = "redacted"
BLOCKED = "blocked"
@dataclass
class BusinessAssociate:
"""HIPAA Business Associate Agreement (BAA) holder"""
ba_id: str
name: str
agreement_signed: datetime
agreement_expiry: datetime
permitted_uses: List[str]
phi_access_level: PHIHandlingLevel
def is_agreement_valid(self) -> bool:
return datetime.now() < self.agreement_expiry
class HIPAAControls:
"""
HIPAA Technical Safeguards Implementation
45 CFR Part 164 - Security and Privacy
"""
def __init__(self, encryption_key: bytes, audit_enabled: bool = True):
self.encryption_key = encryption_key
self.audit_enabled = audit_enabled
self.phi_registry: Dict[str, Dict] = {}
self.business_associates: Dict[str, BusinessAssociate] = {}
self.audit_trail: List[Dict] = []
def encrypt_phi(self, phi_data: Dict, patient_id: str) -> str:
"""AES-256 encryption for PHI at rest"""
# HIPAA: §164.312(a)(2)(iv) Encryption
import base64
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.backends import default_backend
# Generate unique IV
iv = uuid.uuid4().bytes
cipher = Cipher(
algorithms.AES(self.encryption_key),
modes.CBC(iv),
backend=default_backend()
)
encryptor = cipher.encryptor()
# Pad data to block size
data_str = str(phi_data)
padded = data_str + ' ' * (16 - len(data_str) % 16)
encrypted = encryptor.update(padded.encode()) + encryptor.finalize()
# Store IV with encrypted data
storage_key = hashlib.sha256(
f"{patient_id}{datetime.now().isoformat()}".encode()
).hexdigest()[:16]
self.phi_registry[storage_key] = {
"patient_id": self._hash_identifier(patient_id),
"encrypted_data": base64.b64encode(iv + encrypted).decode(),
"created": datetime.now().isoformat(),
"encryption_version": "AES-256-CBC-v