Executive Summary
AI application security is the practice of building and operating software that uses AI without allowing model behavior, untrusted content, data flows, or connected tools to violate security policy. It extends secure software development across prompts, retrieval, models, evaluations, and probabilistic outputs.
What is AI application security?
AI application security is the practice of building and operating software that uses AI without allowing model behavior, untrusted content, data flows, or connected tools to violate security policy. It extends secure software development across prompts, retrieval, models, evaluations, and probabilistic outputs.
The application remains the security boundary. Models should not decide authentication, authorization, or whether a transaction is valid. Teams should threat-model how instructions enter through users, retrieved documents, web content, files, and tools. They should also examine output rendering, excessive agency, sensitive disclosure, dependency provenance, denial of service, and model or prompt changes after release.
Concrete risks
Risk depends on the deployment, its data, its authority, and the consequences of failure. These scenarios are practical starting points for a system-specific assessment, not a claim that every implementation has the same exposure.
- 01Prompt injection can manipulate behavior through direct requests or content the application retrieves.
- 02Insecure output handling can turn generated text into script, SQL, shell commands, or unsafe rendering.
- 03Retrieval can cross user or tenant boundaries when filtering occurs only after semantic search.
- 04Model, library, adapter, prompt, or dataset supply-chain changes can introduce hidden behavior.
- 05Unbounded context, generation, retries, or tool loops can create availability and cost incidents.
Security controls
Controls should be layered so one model error, compromised component, or operator mistake does not directly become a material incident. Each control needs an owner and evidence that it works in the deployed configuration.
- 01Keep authorization and business rules in deterministic code and recheck them at every action boundary.
- 02Separate trusted instructions from untrusted data and design for injection resistance rather than perfect detection.
- 03Encode or validate outputs for their destination; never execute generated code merely because it parses.
- 04Authorize retrieval before access, preserve provenance, and show source evidence for important decisions.
- 05Pin and inventory dependencies, verify artifacts, restrict egress, protect secrets, and review updates.
- 06Run security evaluation in CI and production using normal, adversarial, privacy, and failure scenarios.
Enterprise application
Integrate AI checks into existing architecture review, secure development, deployment, and incident processes. Give teams approved patterns for model gateways, retrieval, tool wrappers, secret handling, logging, and evaluations. Release criteria should state the model and prompt version, permitted data, tool scopes, test results, known limits, owner, and rollback. Runtime monitoring must distinguish model-quality issues from security policy violations.
Alexa Cybersecurity editorial checklist
The following framework is an original editorial synthesis by the Alexa Cybersecurity Editorial Team. It is intended to help teams structure a review. It is not a standard, certification, benchmark, or field-tested research result, and organizations should adapt it to their systems, obligations, and risk appetite.
- 01Document purpose, users, data, model, retrieval, tools, and trust boundaries.
- 02Model OWASP AI risks plus business-specific abuse and failure.
- 03Enforce identity, authorization, validation, isolation, and rate limits outside the model.
- 04Test direct and indirect injection, disclosure, unsafe output, and excessive agency.
- 05Protect software, model, prompt, data, and vendor supply chains.
- 06Operate logging, evaluation, incident handling, rollback, and change review.
Frequently Asked Questions
Q.Can prompt injection be eliminated with an input filter?
A.No. Filters can reduce known patterns but cannot reliably distinguish every instruction from data. Limit authority, isolate content, validate actions, require approval where needed, and assume some injections reach the model.
Q.Is a model API the main application security boundary?
A.No. Security boundaries include user identity, application code, retrieval stores, tool gateways, downstream APIs, output renderers, and operational systems. The application must enforce policy across all of them.