RESEARCH EDUCATIONAL USE ONLY

AI and LLMs introduce key risks in enterprise systems  

Prompt injection  

Data leakage  

Adversarial abuse  

Compliance failures  

Developers must treat LLMs as part of the attack surface and apply layered security controls.  

Security is probabilistic and cannot fully eliminate risk.

CORE PRACTICES  

Validate and sanitize all inputs and outputs  

Enforce strict schemas and least privilege  

Protect sensitive data and avoid exposing secrets  

Continuously monitor for abuse and anomalies  

Use adversarial testing such as injection and jailbreak scenarios  

DEVELOPMENT AND DATA  

Integrate security into the SDLC using OWASP guidance  

Threat model models prompts and retrieval flows using MITRE ATLAS  

Use trusted datasets and minimize sensitive data  

Apply NIST AI RMF and secure development practices  

RUNTIME AND OPERATIONS  

Secure endpoints with authentication rate limiting and controls  

Monitor for scraping prompt abuse and system misuse  

Patch retrain and maintain lifecycle governance  

LLM SPECIFIC RISKS  

Prompt injection cannot be fully prevented only mitigated  

Outputs must always be validated before execution  

Tool and API access must be tightly controlled  

Model behavior can be manipulated through adversarial inputs  

BEST PRACTICES  

Never trust model output without validation  

Never expose credentials or sensitive data  

Do not allow unrestricted code or tool execution  

Use approved and monitored AI systems only  

BOTTOM LINE  

LLMs are powerful but untrusted components  

All outputs must be treated as potentially unsafe  

Security depends on validation isolation and continuous monitoring.