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LLM guardrails: Best practices for deploying LLM apps securely

Datadog Engineering · 22 October 2025 · 12 min read

A post on datadoghq.com, published 22 October 2025, has not been cited by any blog yet (corpus.blog, measured 15 October 2026).

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OWASP Foundation - The Open Source Foundation for Application Security Read at owasp.org

owasp.org

LLM01:2025 Prompt Injection Read at genai.owasp.org

genai.owasp.org

LLM02:2025 Sensitive Information Disclosure Read at genai.owasp.org

genai.owasp.org

LLM07:2025 System Prompt Leakage Read at genai.owasp.org

genai.owasp.org

LLM06:2025 Excessive Agency Read at genai.owasp.org

genai.owasp.org

https://python.langchain.com/docs/concepts/tool_calling/ Who cites this page

python.langchain.com

https://en.wikipedia.org/wiki/Sandbox_(computer_security Who cites this page

en.wikipedia.org

https://learnprompting.org/docs/prompt_hacking/offensive_measures/dan?srsltid=AfmBOorlYXnCS-b6Ec3GlxkHOtxzsessdRf68JH4YNTEA-uMVJwDcJQ0 Who cites this page

learnprompting.org

https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-86M Who cites this page

huggingface.co

https://docs.aws.amazon.com/prescriptive-guidance/latest/llm-prompt-engineering-best-practices/common-attacks.html Who cites this page

docs.aws.amazon.com

https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-content-filters.html Who cites this page

docs.aws.amazon.com

https://promptarmor.substack.com/p/slack-ai-data-exfiltration-from-private Who cites this page

promptarmor.substack.com

https://docs.slack.dev/ai/using-data-access-api/ Who cites this page

docs.slack.dev

Elsewhere on datadoghq.com (9)

Best practices for monitoring LLM prompt injection attacks to protect sensitive data Read at datadoghq.com

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Abusing AI infrastructure: How mismanaged credentials and resources expose LLM applications Read at datadoghq.com

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Abusing supply chains: How poisoned models, data, and third-party libraries compromise AI systems Read at datadoghq.com

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Abusing AI interfaces: How prompt-level attacks exploit LLM applications Read at datadoghq.com

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Understanding MCP security: Common risks to watch for Read at datadoghq.com

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Get granular LLM observability by instrumenting your LLM chains Read at datadoghq.com

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https://docs.datadoghq.com/llm_observability/ Who cites this page

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Building an LLM evaluation framework: best practices Read at datadoghq.com

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https://www.datadoghq.com/product-preview/ai-security/

datadoghq.com

LLM guardrails: Best practices for deploying LLM apps securely

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