AI Security Platform Engineering

Deploying AI models in production introduces unique security challenges. Traditional application security doesn't cover prompt injection, model poisoning, or data exfiltration through LLM outputs.

The AI Threat Landscape

OWASP's Top 10 for LLM Applications highlights critical risks:

1. Prompt injection — Malicious inputs manipulate model behavior
2. Insecure output handling — LLM responses executed as code
3. Training data poisoning — Corrupted data produces biased/harmful outputs
4. Model denial of service — Resource-intensive prompts crash systems
5. Supply chain vulnerabilities — Compromised model weights or dependencies

Defense-in-Depth Architecture

┌─────────────────────────────────────────┐
│           Input Guardrails              │
│  (prompt validation, PII detection,     │
│   injection patterns, rate limiting)    │
├─────────────────────────────────────────┤
│           AI Gateway / Proxy            │
│  (authentication, routing, logging,     │
│   cost controls, model selection)       │
├─────────────────────────────────────────┤
│           Model Runtime                 │
│  (sandboxed execution, resource limits, │
│   tool use restrictions)                │
├─────────────────────────────────────────┤
│           Output Guardrails             │
│  (content filtering, PII scrubbing,     │
│   hallucination detection, citations)   │
└─────────────────────────────────────────┘

Implementing Input Guardrails

Validate all inputs before they reach the model:

# Example guardrail checks
def validate_prompt(prompt: str) -> bool:
    checks = [
        not contains_injection_patterns(prompt),
        not contains_pii(prompt),
        len(prompt) < MAX_PROMPT_LENGTH,
        not is_jailbreak_attempt(prompt),
    ]
    return all(checks)

def contains_injection_patterns(text: str) -> bool:
    patterns = [
        r"ignore previous instructions",
        r"you are now",
        r"system prompt",
        r"repeat after me",
    ]
    return any(re.search(p, text, re.I) for p in patterns)

Model Access Controls

Implement RBAC for AI model access:

• Per-model permissions — Not all users need access to all models
• Rate limiting per user/team — Prevent cost overruns and DoS
• Data classification enforcement — Sensitive data only goes to approved models
• Audit logging — Every prompt and response logged for compliance

Kubernetes Security for AI

Securing AI workloads on Kubernetes:

apiVersion: v1
kind: Pod
metadata:
  name: model-server
spec:
  securityContext:
    runAsNonRoot: true
    seccompProfile:
      type: RuntimeDefault
  containers:
  - name: model
    securityContext:
      allowPrivilegeEscalation: false
      readOnlyRootFilesystem: true
      capabilities:
        drop: ["ALL"]
    resources:
      limits:
        nvidia.com/gpu: 1
        memory: "16Gi"
      requests:
        memory: "8Gi"

Observability for AI Security

Monitor these signals:

• Prompt anomaly detection — Flag unusual input patterns
• Output toxicity scores — Track content safety metrics
• Token usage spikes — Potential abuse or injection attacks
• Latency outliers — May indicate adversarial prompts
• Error rates by model — Failing guardrails indicate attack attempts

FAQ

Is prompt injection really that serious? Yes. Prompt injection can exfiltrate data, bypass access controls, and manipulate downstream systems that trust LLM outputs.

Should I build guardrails in-house or use a vendor? Start with vendors (AWS Bedrock Guardrails, Azure AI Content Safety) and customize as your threat model matures.

How do I test AI security? Use red-teaming tools like Garak or PyRIT. Run adversarial prompt suites as part of CI/CD.

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