Security by Design for AI‑Powered Apps: A Practical Checklist

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Security by Design for AI‑Powered Apps: A Practical Checklist

AI capabilities are now embedded in customer support portals, analytics dashboards, mobile apps, and developer tooling. With this power comes an expanded attack surface: model endpoints, vector databases, orchestration layers, third‑party APIs, and prompts. Building AI‑enabled systems securely means adopting Security by Design from the first commit—not as a last‑minute compliance fix.

Why it matters: according to IBM’s 2024 Cost of a Data Breach Report, the global average breach cost exceeded $4.8 million, with detection and escalation continuing to be the largest cost center (IBM). The human element remains a dominant factor in breaches—roughly three‑quarters involve social engineering, error, or misuse (Verizon DBIR). AI surfaces new human‑in‑the‑loop risks—like prompt injection—that require explicit defensive patterns.

What Security by Design Means for AI Apps

Security by Design is the practice of making security a default requirement in architecture and code, guided by principles like least privilege, fail‑safe defaults, and complete mediation—concepts that trace back to Saltzer and Schroeder’s 1975 work on protection systems (MIT). For AI systems, these principles translate into minimizing sensitive data exposure to models, validating and constraining all inputs, isolating model tools and connectors, and verifying every cross‑component interaction.

Developer‑First Security Checklist (Mapped to OWASP ASVS/MASVS)

1) Data minimization

  • Collect and process only the data necessary for the task. Mask, tokenize, or anonymize before it reaches a model or vector store (e.g., replace emails with hashes or surrogate keys).
  • Segment indexes by tenant; avoid global embeddings that mix data across customers.
  • Apply purpose‑based access: authorization decisions must reflect context and user intent. Deny by default.
  • Encrypt data in transit (TLS 1.2+) and at rest (KMS‑managed keys). Consider field‑level or searchable encryption for sensitive attributes.

Standards mapping: OWASP ASVS V1 Architecture and Design, V9 Data Protection; NIST Privacy Framework data minimization (NIST); OWASP MASVS MASVS‑ARCH, MASVS‑STORAGE.

2) Model input validation

  • Validate all user and system inputs before they reach the model gateway: enforce max length, character sets, MIME types, and JSON schemas.
  • Normalize and canonicalize inputs (e.g., Unicode normalization, newline stripping) to prevent filter bypass.
  • Use allowlists for tools/actions a model may invoke; reject unknown tool names or parameters.
  • Segment RAG retrieval queries so they cannot arbitrarily broaden scope (restrict indices, namespaces, and user context).

Standards mapping: OWASP ASVS V5 Validation, Sanitization and Encoding; OWASP API Security Top 10 A4: Unrestricted Resource Consumption, A8: Injection (OWASP API).

3) Prompt injection defenses

  • Isolate untrusted content from system prompts. Wrap external content with explicit disclaimers and transformations (e.g., HTML to text, stripping scripts).
  • Use role‑segmented prompts: keep system directives immutable; pass user content separately; never let the model rewrite safety policies.
  • Constrain tool use with strict schemas and policy checks; implement out‑of‑band authorization for dangerous actions (e.g., payments, file writes).
  • Run automated red‑teaming with adversarial prompts prior to release and after major updates.

Standards mapping: OWASP Top 10 for LLM Applications (LLM01 Prompt Injection, LLM06 Sensitive Information Disclosure) (OWASP LLM); ASVS V2 Authentication and V4 Access Control for tool invocation boundaries.

4) Secrets management

  • Use a centralized secrets manager (e.g., HSM/KMS or Vault). Never hardcode keys or store them in prompts or datasets.
  • Rotate credentials automatically; prefer short‑lived tokens and workload identity over long‑lived API keys.
  • Scope credentials minimally—per environment, per service, per tenant.
  • Block secrets from logs, traces, and crash reports; add pre‑commit and CI scanners.

Standards mapping: OWASP ASVS V2.7 Secrets Management, V9 Data Protection; MASVS MASVS‑STORAGE; NIST SP 800‑53 IA/AC family.

5) Secure model endpoints and orchestration

  • Require mTLS or OAuth 2.0/OIDC between app, gateway, and model endpoints; bind tokens to audience and scope.
  • Apply rate limiting, concurrent session limits, and per‑tenant quotas to prevent resource exhaustion.
  • Enforce egress policies: the model process can only call approved hosts and tools via a proxy with DNS allowlists.
  • Use content security policies for model‑generated HTML/UI; sandbox renderers in webviews for mobile.

Standards mapping: OWASP ASVS V2 Authentication, V3 Session Management, V10 Communications; MASVS‑NETWORK; OWASP API Security A5: Broken Function Level Authorization.

6) Telemetry redaction and safe logging

  • Define a data classification and logging policy. Redact PII/PHI from prompts, embeddings, and responses before storage.
  • Use structured logs; store only hashes or tokens for lookups. Disable body logging on reverse proxies for sensitive paths.
  • Apply privacy‑preserving analytics where possible (aggregation, differential privacy for metrics).
  • Protect logs with encryption and access controls; define retention and deletion windows.

Standards mapping: OWASP ASVS V7 Error Handling and Logging, V9 Data Protection; NIST Privacy Framework; MASVS‑RESILIENCE.

Example Policies You Can Adopt Today

Prompt Logging Policy

{
  "policy": "No raw prompts or model responses containing PII/PHI are stored.",
  "controls": [
    "PII detector runs pre‑log; sensitive fields tokenized via format‑preserving tokens",
    "Requests/responses sampled at <1% with irreversible redaction",
    "Security team can temporarily raise sampling with DPO approval"
  ]
}

Model Tool Invocation Policy

{
  "allowed_tools": ["search", "retrieve_invoice", "create_support_ticket"],
  "guardrails": {
    "create_support_ticket": {
      "schema": {"title": "string<=120", "priority": "low|medium|high"},
      "authorization": "user must have support:write",
      "rate_limit": "5/min per user"
    }
  },
  "deny": ["file_system.write", "payment.submit"],
  "breakglass": {"approval_required": true, "audited": true}
}

Secrets Handling Policy

principles:
  - all secrets stored in a vault with auto‑rotation
  - no secrets in prompts, datasets, or embeddings
  - ephemeral credentials for CI jobs (OIDC‑based)
  - deny log ingestion of Authorization headers and cookies

controls:
  - pre‑commit: gitleaks + trufflehog
  - CI: block on any new secret detection; rotation playbook auto‑triggers

CI/CD Security Gates for AI‑Powered Apps

  • Pre‑commit: secret scanning (e.g., Gitleaks, TruffleHog), linting for unsafe prompt patterns (e.g., Semgrep custom rules).
  • Build: dependency and SBOM generation (Syft + CycloneDX), SCA scans (e.g., OWASP Dependency‑Track, Snyk).
  • Infrastructure as Code: policy‑as‑code for cloud and K8s (OPA/Gatekeeper, Kyverno), misconfig scans (Checkov/Terraform validators).
  • DAST and API scanning: OWASP ZAP, contract tests for model gateways and RAG endpoints.
  • AI safety tests: adversarial prompt suites (NeMo Guardrails, promptfoo, OpenAI Evals), jailbreak detection and content safety evaluation.
  • Release: manual approval if risk score exceeds threshold; ensure model/version and SBOM are recorded for traceability.
  • Deploy: enforce mTLS via service mesh (Istio/Linkerd), enforce egress allowlists.
  • Post‑deploy: privacy‑preserving telemetry, anomaly detection on token use, and continuous red‑team canary prompts.

Real‑World Patterns and Pitfalls

  • Prompt injection via retrieved content: When integrating web or knowledge base retrieval with LLMs, malicious content can instruct the model to exfiltrate secrets or ignore policies. Defenses include content sanitization, tool allowlists, and strict retrieval scoping. See OWASP’s LLM01 guidance for detailed controls (OWASP LLM Top 10).
  • Over‑permissive tools: Connecting file systems or payment APIs directly to model outputs without out‑of‑band authorization invites abuse. Apply just‑in‑time approvals and dual‑control for high‑risk actions.
  • Shadow logging of sensitive data: Debug logs often inadvertently persist tokens and PII. Adopt a default‑deny log policy and verify with automated tests. NIST’s Privacy Framework provides useful guidance on minimization and governance (NIST).

Quick Reference: Mapping to OWASP MASVS/ASVS

  • Architecture and threat modeling: ASVS V1; MASVS‑ARCH.
  • Authentication, session, and access control: ASVS V2–V4; MASVS‑AUTH.
  • Input validation and output encoding: ASVS V5; aligns with LLM01/LLM06.
  • Cryptography, data protection, secrets: ASVS V6/V9; MASVS‑STORAGE.
  • Error handling, logging, and privacy: ASVS V7/V9; MASVS‑RESILIENCE.
  • Communications security (mTLS, TLS): ASVS V10; MASVS‑NETWORK.

Key Concepts Explained

App security: The end‑to‑end practice of safeguarding code, infrastructure, data, and users across web and mobile apps. For AI‑enabled products, app security extends to model gateways, orchestration, and third‑party tools, ensuring that traditional controls—like input validation, access control, and secure session handling—also mediate model interactions and generated actions.

AI security: A discipline focused on protecting AI systems from adversarial manipulation (evasion, poisoning, prompt injection), safeguarding model supply chains, and preserving data confidentiality and integrity. It also includes safe deployment practices like content safety filtering and tool isolation. See NIST’s AI Risk Management Framework for a holistic approach (NIST AI RMF 1.0).

OWASP: The Open Worldwide Application Security Project publishes vendor‑neutral guidance such as the ASVS, MASVS, API Security Top 10, and the LLM Top 10. These resources are the baseline for aligning engineering practices and security testing with recognized controls (OWASP.org).

Prompt injection: An attack where untrusted content manipulates a model to ignore safety rules, exfiltrate secrets, or execute unintended tool actions. Prevent it with isolation, allowlists, schema‑constrained tools, and automated red‑teaming. OWASP lists prompt injection as the top LLM risk (OWASP LLM Top 10).

Secrets management: The secure creation, storage, rotation, and auditing of credentials and keys. Centralize secrets in a vault, use short‑lived credentials, prevent logging or embedding in prompts, and automate detection in CI (Vault, Gitleaks).

Secure coding: Writing code that defaults to least privilege, validates inputs, encodes outputs, handles errors safely, and minimizes data. In AI pipelines, secure coding means constraining model tools, validating schemas, and ensuring every cross‑boundary call has explicit authorization.

MASVS: The Mobile Application Security Verification Standard defines mobile‑specific controls for architecture, storage, cryptography, network security, and resilience. It is essential when deploying AI features in iOS/Android apps—for example, sandboxing LLM‑powered UI and enforcing device attestation (OWASP MASVS).

ASVS: The Application Security Verification Standard is a control catalog for web and API applications, used to define what “secure enough” means at Levels 1–3. Mapping your backlog to ASVS ensures coverage of architecture, authentication, access control, validation, data protection, logging, and communications (OWASP ASVS).

CI/CD security: Security controls embedded in the build and release process—secret scanning, SAST/SCA, IaC policy gates, DAST, and AI safety tests. Security should block merges when risk thresholds are exceeded and record model versions and SBOMs for auditability.

Data minimization: A privacy and security principle that reduces breach impact by collecting, processing, and storing only what is necessary, for as short a time as needed. In AI, it means pre‑processing to strip PII before embedding, isolating tenants, and auditing what gets logged or cached.

Putting It Together: A Minimal Secure Architecture

  • Edge/API: OAuth2/OIDC auth, request size and rate limits, schema validation.
  • Orchestrator: Immutable system prompt + separate user content; tool calls constrained by policy and a permission service.
  • RAG layer: Tenant‑scoped indices; pre‑processing pipeline that removes PII; egress allowlists.
  • Model gateway: mTLS, fine‑grained scopes, prompt/response redaction for telemetry.
  • Observability: Structured logs, sensitive‑field hashing, privacy‑aware metrics dashboards.

Where to Go Next

If you’re introducing AI into a web or mobile product, start by mapping your backlog to ASVS/MASVS controls and implementing the checklist above. For teams building high‑load Next.js or full‑stack services, strong boundaries and performance‑aware guardrails are equally critical. Explore our related pages: Next.js developers and full‑stack developers.

Want a pragmatic security review of your AI‑powered application—architecture, prompts, data flows, and CI/CD gates? Request a developer‑first assessment from Teyrex. We help teams design and ship high‑load, reliable, and secure web and mobile applications with AI inside.