Full Report
Extending the Wiz AI APP into the code layer to detect AI-specific risks at inception, validate exploitability at runtime, and orchestrate remediation with agents that understand your codebase
Analysis Summary
# Best Practices: Secure AI Application Development (Prompt-to-Production)
## Overview
These practices address the unique security challenges of AI-native and agentic applications. They focus on bridging the gap between static code analysis (SAST) and runtime exploitability, ensuring that AI-specific vulnerabilities—such as prompt injection and insecure output handling—are identified during the design phase and validated in production.
## Key Recommendations
### Immediate Actions
1. **Deploy IDE Security Extensions:** Integrate security scanning into developer environments (VS Code, JetBrains, Cursor) to provide real-time feedback on AI-specific code risks.
2. **Audit AI Input Points:** Manually review code where user input is passed directly into Large Language Model (LLM) prompts or AI agents.
3. **Implement Secrets Scanning:** Use automated tools to scan for hardcoded API keys for AI services (OpenAI, Anthropic, etc.) within repositories.
### Short-term Improvements (1-3 months)
1. **Map to Industry Benchmarks:** Standardize code scanning policies against the **OWASP Top 10 for LLM Applications** and **OWASP Top 10 for Agentic Applications**.
2. **Unify AI Policy Management:** Ensure the same security rules used in development (SAST) are applied to cloud workloads and runtime environments to maintain consistency.
3. **Automate SCA for AI Libraries:** Perform Software Composition Analysis (SCA) specifically targeting popular AI/ML frameworks to detect vulnerable versions of dependencies.
### Long-term Strategy (3+ months)
1. **Implement Runtime Exploitability Validation:** Transition from "alert-only" security to "automated validation" by using AI-powered red teaming agents to probe live endpoints for exploitability.
2. **Establish Agentic Remediation:** Deploy AI security agents that understand the codebase to orchestrate automated remediation of identified vulnerabilities.
3. **Design "AI Inception" Workflows:** Create a security fabric where Every line of AI-generated code is automatically scanned before it is committed to the main branch.
## Implementation Guidance
### For Small Organizations
- Focus on **IDE-based scanning** to catch errors during development.
- Prioritize **Secrets Scanning** to prevent the exposure of expensive or sensitive AI API keys.
- Use free resources like the OWASP LLM Top 10 to educate developers.
### For Medium Organizations
- Implement a **Unified Policy Engine** to connect code-level risks to cloud infrastructure.
- Automate **Infrastructure as Code (IaC) scanning** for the environments hosting your AI models.
- Conduct quarterly vulnerability assessments focusing on "Shadow Data"—stale or duplicated data used for model training.
### For Large Enterprises
- Deploy **Red Teaming Agents** to continuously probe AI endpoints in production.
- Integrate security scanning into **CI/CD pipelines** to block deployments that violate AI safety policies.
- Map all security findings to a centralized dashboard for Global Compliance and Risk Management.
## Configuration Examples
While specific code snippets vary by platform, organizations should configure their SAST engines with rules such as:
- `RULE_ID: AI_PROMPT_INJECTION_SENSITIVE`: Detects unsanitized user inputs being concatenated into system prompts.
- `RULE_ID: AI_AGENT_TOOL_UNRESTRICTED`: Detects AI agents with access to sensitive system tools/APIs without sufficient permission gating.
## Compliance Alignment
- **OWASP Top 10 for LLM Applications (2025):** Focuses on prompt injection, data leakage, and excessive agency.
- **OWASP Top 10 for Agentic Applications (2026):** Focuses on risks inherent to autonomous AI agents and tool use.
- **NIST AI Risk Management Framework (RMF):** General guidance on AI safety and security.
## Common Pitfalls to Avoid
- **Alert Fatigue:** Focusing on every "vulnerability" without validating if it is actually reachable via a public API or internet-facing endpoint.
- **The LLM Blind Spot:** Assuming traditional web security (like SQL injection filters) will automatically catch AI-specific risks like "Indirect Prompt Injection."
- **Neglecting Shadow Data:** Securing the code but leaving sensitive, unmanaged datasets exposed for AI training or inference.
## Resources
- **OWASP Top 10 LLM Project:** hxxps[://]owasp[.]org/www-project-top-10-for-large-language-model-applications/
- **Wiz AI-APP Research:** hxxps[://]www[.]wiz[.]io/blog/introducing-wiz-ai-app
- **NIST AI Framework:** hxxps[://]www[.]nist[.]gov/itl/ai-risk-management-framework