As generative AI rapidly integrates into enterprise ecosystems, the need for robust guardrails has become a critical concern for organizations seeking to leverage this powerful technology responsibly. This chapter provides a comprehensive framework for designing, implementing, and governing AI guardrails to ensure safety, fairness, control, and compliance in high-stakes business environments. The chapter begins by contextualizing the urgent need for responsible AI in enterprise settings, emphasizing risks such as hallucinations, bias, and privacy breaches. It highlights how unguarded generative AI systems can not only produce harmful outputs but also expose organizations to regulatory penalties, reputational damage, and operational inefficiencies. With regulations such as the EU Artificial Intelligence Act imposing strict governance requirements on high-risk AI applications, enterprises must adopt proactive measures to maintain compliance and public trust. The core of the chapter explores a multilayered approach to AI guardrails, encompassing technical, procedural, and human oversight mechanisms. At the technical level, input filtering (e.g., regex and ML classifiers), output validation (e.g., schema enforcement, toxicity detectors), and grounding with enterprise knowledge are emphasized as vital components in limiting model misbehavior. Reinforcement Learning from Human Feedback (RLHF) and constitutional AI techniques are explored as strategies to tune models toward ethical and organizational values. Tools such as Guardrails AI, PromptFoo, and Amazon Bedrock Guardrails provide tangible implementation pathways for enterprises. On the procedural side, the chapter outlines governance frameworks—policies, ethics committees, and compliance programs—that define roles and responsibilities across the AI lifecycle. It stresses the importance of policy-as-code to embed these rules into CI/CD workflows, enabling scalable and automated enforcement. A hybrid architecture combining automated enforcement with human-in-the-loop (HITL) mechanisms is advocated to catch edge cases and ensure nuanced decision-making where AI models fall short. Enterprise case studies provide compelling validation of these approaches. Banks have used real-time guardrails to prevent the leakage of personally identifiable information (PII), while healthcare providers rely on schema validators to filter out unsafe medical advice. These examples demonstrate not only reduced risk exposure and improved regulatory compliance but also tangible business value, such as faster audit readiness and enhanced customer trust. A key innovation discussed in the chapter is knowledge grounding through retrieval-augmented generation (RAG), which ties generative outputs to authoritative corporate sources. This technique significantly reduces hallucination rates and enables auditability—every output can be traced back to verifiable documents. Combined with output classifiers and HITL review, grounding transforms generative AI into a more trustworthy and explainable asset within the enterprise. The chapter also emphasizes measurable impact and continuous improvement as design principles for guardrails. Enterprises are encouraged to monitor metrics such as guardrail trigger counts, hallucination frequency, and fairness indices. Tools like MLflow, Evidently AI, and OpenLineage facilitate comprehensive auditing and traceability across model training and inference. These mechanisms support iterative guardrail refinement, aligning with the National Institute of Standards and Technology (NIST) AI Risk Management Framework’s “Measure” and “Manage” functions. Finally, the chapter outlines governance best practices and organizational structures to support responsible AI. From the Chief AI Ethics Officer to compliance managers and ML engineers, each role has a defined stake in ensuring that generative AI operates within safe, legal, and ethical boundaries. Global regulations, including the EU AI Act, U.S. agency guidelines, and China’s Interim Measures, are discussed to provide readers with an international compliance perspective. In conclusion, this chapter offers enterprise leaders, AI practitioners, and policy advocates a blueprint for operationalizing responsible AI through layered guardrails. It equips organizations with the tools, strategies, and governance models necessary to mitigate risks, maximize business value, and build stakeholder trust in the age of generative AI. As organizations scale AI across functions, these guardrails serve not as constraints but as enablers—empowering safe, innovative, and resilient AI adoption across industries.