As AI and Large Language Models (LLMs) become embedded into enterprise applications, security risks increase: prompt injection, data leakage, adversarial abuse, and compliance failures. Developers are on the front line of defending against these risks. 

This guide distills trusted frameworks from OWASP, NIST, MITRE, and ISO into actionable best practices tailored for developers.

• Treat AI/LLM integrations as part of the attack surface.
• Apply input/output validation, schema enforcement, and least privilege.
• Use adversarial testing and continuous monitoring.
• Protect sensitive data and align with compliance frameworks.
• Follow secure prompting practices to prevent misuse.
• Leverage checklists and quick references for day-to-day secure development.

- Integrate security into the SDLC using OWASP AI Security & Privacy Guide and OWASP LLM Top 10.

- Threat model AI models, prompts, and retrieval chains with MITRE ATLAS patterns.

- Validate and sanitize inputs to AI and tool calls (escape control tokens, enforce schemas).

- Apply least privilege to model keys, vector stores, and plugins.

- Validate datasets for poisoning/backdoors; prefer trusted/curated sources.

- Minimize sensitive data (tokenization, hashing, synthetic data).

- Apply NIST AI RMF data governance principles.

- Apply NIST SP 800-218A (SSDF for GenAI) in CI/CD pipelines.

- Use OWASP AI Testing Guide for component assessment.

- Conduct adversarial testing: injection, jailbreaking, data exfiltration, model evasion.

- Apply MITRE ATLAS techniques for threat-informed testing.

- Validate outputs for bias, safety, and PII leakage.

- Protect endpoints: strong authentication, mTLS, rate limits, token budget guards.

- Monitor for abuse: prompt spray, scraping, DoS, retrieval poisoning.

- Patch/retrain regularly; document lifecycle per ISO/IEC 5338.

- Apply data minimization/purpose limitation.

- Map compliance obligations (GDPR, HIPAA) and provide delete/export.

- Align with ISO/IEC 42001 (AI governance).

- Extend IR playbooks for AI incidents (prompt injection, unsafe tool use).

- Share lessons learned with MITRE ATLAS mapping.

- Align with CISA and ENISA guidance for secure AI deployment.

- Prompt Injection Defense: sanitize inputs, enforce hierarchy, use content filters.

- Context Control: scope system prompts; never expose raw secrets.

- Output Filtering: apply moderation and schema validation before execution.

- Fine-Tuning Security: vet datasets, license, and consent.

- Abuse Monitoring: detect scraping, brute force, and overreach.

- Explainability: publish model cards documenting risks and limitations.

Safe: Use LLM to generate code under strict JSON schema + guardrails.

Unsafe: Executing arbitrary shell/code directly from model output.

Safe: Summarize redacted documents; store embeddings only of sanitized text.

Unsafe: Sending PII, credentials, or IP to untrusted third-party models.

DO:
• State constraints (schemas, token limits).
• Use system prompts to enforce rules.
• Sanitize user content.
• Post-process outputs with validators.

DON’T:
• Don’t trust outputs blindly.
• Don’t allow prompts to override policies.
• Don’t expose secrets in prompts.
• Don’t let LLMs call sensitive functions without guardrails.

Pre-Deployment: Threat modeling, redaction, red-team tests, schema validation.

Runtime: Centralized logging, drift monitoring, key rotation, retrain/patch cycle.

Governance: Map practices to NIST AI RMF and ISO standards.

Top 5 LLM Security Pitfalls:
1. Prompt injection
2. Data leakage
3. Insecure tool execution
4. Supply chain poisoning
5. Lack of monitoring

Top 5 Secure Prompting Rules:
1. Always enforce schemas
2. Define clear system roles
3. Sanitize/escape user content
4. Validate outputs
5. Never expose secrets

OWASP AI Security & Privacy Guide – 

https://owasp.org/www-project-ai-security-and-privacy-guide/

OWASP AI Testing Guide – https://owasp.org/www-project-ai-testing-guide/

OWASP LLM Top 10 – https://owasp.org/www-project-top-10-for-large-language-model-applications/

OWASP GenAI Security Project – https://genai.owasp.org/

NIST AI RMF 1.0 – https://www.nist.gov/itl/ai-risk-management-framework

NIST Generative AI Profile – https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf

NIST SP 800-218A SSDF Profile for GenAI – https://csrc.nist.gov/pubs/sp/800/218/a/final

ISO/IEC 42001, 23894, 5338 (AI governance, risk management, lifecycle)

CISA Guidance – https://www.cisa.gov/

NCSC Guidelines – https://www.ncsc.gov.uk/

MITRE ATLAS – https://atlas.mitre.org/

ENISA Threat Landscape – https://www.enisa.europa.eu/topics/threat-risk-management/threats-and-trends

- Prompt Injection and Jailbreaking: Attackers can override instructions, exfiltrate data, or execute malicious actions.
- Data Leakage: Sensitive data (PII, credentials, IP) exposed via prompts or outputs.
- Model Supply Chain Poisoning: Backdoored datasets, libraries, or fine-tunes compromising integrity.
- Insecure Tool/Function Calls: LLMs triggering system commands or APIs without guardrails.
- Adversarial Exploits: Evasion, inversion, or abuse leading to manipulated outcomes.
- Lack of Monitoring: No visibility into misuse, drift, or abnormal patterns.

- Never input sensitive information (e.g., credentials, personal data, trade secrets) into LLMs unless controls are in place.
- Be cautious of outputs — always verify critical information before acting on it.
- Follow corporate policies on acceptable use of AI tools.
- Treat LLMs as untrusted assistants: validate their work, don’t rely blindly.
- Avoid re-sharing AI outputs without checking for hallucinations or bias.
- Report suspicious or unsafe behavior from AI tools to the security team immediately.
- Use company-approved AI integrations rather than unvetted third-party tools.