القائمة
Security best practices for deploying AI/ML models to production environments
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عرض SKILL.md
CI/CD integration and automation frameworks for continuous AI security testing
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| name | secure-deployment |
| version | 2.0.0 |
| description | Security best practices for deploying AI/ML models to production environments |
| sasmp_version | 1.3.0 |
| bonded_agent | 06-api-security-tester |
| bond_type | SECONDARY_BOND |
| input_schema | {"type":"object","required":["deployment_stage"],"properties":{"deployment_stage":{"type":"string","enum":["pre_deployment","deployment","runtime","all"]},"environment":{"type":"string","enum":["development","staging","production"]}}} |
| output_schema | {"type":"object","properties":{"security_score":{"type":"number"},"checks_passed":{"type":"integer"},"checks_failed":{"type":"integer"},"recommendations":{"type":"array"}}} |
| owasp_llm_2025 | ["LLM03","LLM06"] |
| nist_ai_rmf | ["Govern","Manage"] |
Deploy AI/ML models securely with defense-in-depth strategies and zero-trust architecture.
Skill: secure-deployment
Agent: 06-api-security-tester
OWASP: LLM03 (Supply Chain), LLM06 (Excessive Agency)
NIST: Govern, Manage
Use Case: Secure production deployment
Model Training → [Security Scan] → [Signing] → [Encrypted Storage]
↓
[Canary Deploy] ← [Staged Rollout] ← [Integrity Check] ← [Pull]
↓
[Production] → [Continuous Monitoring]
Security Scans:
- model_vulnerability_scan
- dependency_audit
- bias_evaluation
- adversarial_robustness_test
- pii_leak_detection
- license_compliance
- secrets_detection
class PreDeploymentChecker:
def run_all_checks(self, model_path):
results = []
# Dependency audit
results.append(.audit_dependencies(model_path))
results.append(.scan_for_secrets(model_path))
results.append(.detect_pii_leakage(model_path))
results.append(.test_robustness(model_path))
results.append(.evaluate_bias(model_path))
results
():
vulns = .dependency_scanner.scan(path)
critical = [v v vulns v.severity == ]
critical:
CheckResult(, , critical)
CheckResult(, )
():
secrets = .secret_scanner.scan(path)
secrets:
CheckResult(, , secrets)
CheckResult(, )
Container Security:
base_image: distroless/python3
user: nonroot (UID 65532)
filesystem: read-only
capabilities: drop ALL
seccomp: runtime/default
Network Security:
ingress: API gateway only
egress: allowlist only
mtls: required
network_policy: strict
Secrets Management:
provider: HashiCorp Vault
injection: sidecar
rotation: 24 hours
never_in_env: true
Model Storage:
encryption: AES-256-GCM
signing: RSA-4096
integrity: SHA-256 hash
access: RBAC enforced
# Kubernetes deployment security
SECURE_DEPLOYMENT = """
apiVersion: apps/v1
kind: Deployment
spec:
template:
spec:
securityContext:
runAsNonRoot: true
runAsUser: 65532
fsGroup: 65532
seccompProfile:
type: RuntimeDefault
containers:
- name: model-server
image: distroless/python3:nonroot
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
capabilities:
drop: ["ALL"]
resources:
limits:
cpu: "4"
memory: "16Gi"
nvidia.com/gpu: "1"
requests:
cpu: "2"
memory: "8Gi"
volumeMounts:
- name: model
mountPath: /model
readOnly: true
- name: tmp
mountPath: /tmp
"""
Isolation:
runtime: gvisor
network: namespace isolated
process: pid namespace
Monitoring:
logging: structured JSON
metrics: Prometheus
tracing: OpenTelemetry
alerts: PagerDuty
Resource Protection:
cpu_limit: enforced
memory_limit: enforced
gpu_memory: enforced
timeout: 30 seconds
class RuntimeProtection:
def __init__(self):
self.timeout = 30 # seconds
self.max_memory = 16 * 1024**3 # 16GB
self.rate_limiter = RateLimiter()
def protected_inference(self, model, input_data, user_id):
# Rate limiting
if not self.rate_limiter.allow(user_id):
raise RateLimitError()
# Timeout protection
with timeout(self.timeout):
# Memory monitoring
with memory_limit(self.max_memory):
result = model.infer(input_data)
# Log the request
self.log_inference(user_id, input_data, result)
return result
Rollout Strategy:
canary:
initial_percentage: 5%
increment: 10%
interval: 1 hour
success_criteria:
- error_rate < 0.1%
- latency_p99 < 5s
- no_security_alerts
rollback:
automatic: true
triggers:
- error_rate > 1%
- security_alert
- latency_p99 > 10s
Pre-Deployment:
- [ ] Dependencies scanned and patched
- [ ] Secrets removed from codebase
- [ ] PII leak testing passed
- [ ] Adversarial robustness validated
- [ ] Model signed and verified
- [ ] Access controls configured
Deployment:
- [ ] Non-root container
- [ ] Read-only filesystem
- [ ] Resource limits set
- [ ] Network policies applied
- [ ] Secrets via vault
- [ ] TLS/mTLS enabled
Runtime:
- [ ] Monitoring enabled
- [ ] Alerting configured
- [ ] Logging comprehensive
- [ ] Rate limiting active
- [ ] Rollback tested
# .github/workflows/secure-deploy.yml
name: Secure Deployment
jobs:
security-scan:
steps:
- name: Dependency Audit
run: pip-audit --strict
- name: Secret Scan
run: gitleaks detect
- name: Container Scan
run: trivy image $IMAGE
- name: SBOM Generation
run: syft $IMAGE -o spdx-json
deploy:
needs: security-scan
steps:
- name: Sign Image
run: cosign sign $IMAGE
- name: Verify Signature
run: cosign verify $IMAGE
- name: Deploy Canary
run: kubectl apply -f canary.yaml
CRITICAL:
- Secrets in codebase
- Critical vulnerabilities
- No authentication
HIGH:
- Root container
- Missing encryption
- No rate limiting
MEDIUM:
- Missing resource limits
- Incomplete logging
- Outdated dependencies
LOW:
- Non-optimal configs
- Missing SBOM
Issue: Deployment failing security scan
Solution: Update dependencies, remove secrets, fix configs
Issue: Container won't start (read-only FS)
Solution: Use tmpfs for temp files, volume for model
Issue: High latency after security layers
Solution: Optimize validation, use caching, async logging
| Component | Purpose |
|---|---|
| Agent 06 | Security testing |
| Agent 08 | CI/CD automation |
| /test api | Pre-deploy testing |
| ArgoCD | GitOps deployment |
Deploy AI models securely with defense-in-depth practices.