4 Best Practices for Robust Agentic AI Governance

As agentic AI solutions take the driver’s seat in enterprise operations, robust governance is no longer optional – it is your organisation’s seatbelt for secure, scalable, and seamless adoption.

July. 2, 2025

With 74% of organisations ramping up their AI investments in 2025, agentic AI solutions are gaining traction as a means to optimise operations, elevate customer experience, and accelerate digital transformation at scale. With a multitude of high-impact use cases across functions, their potential in driving disruptive growth is significant. However, responsible adoption remains mission-critical, particularly in the face of rising cyber threats, integrity issues, and complex security risks. Robust agentic AI governance frameworks are pre-requisites to ensure safe deployment, build trust, and avoid downstream complexity as the technology expands from experimentation to enterprise-scale implementation.

As AI agents begin to make autonomous decisions and interact across systems, organisations must adopt a proactive approach and establish strong controls from the outset. This includes defining clear implementation guidelines, assessing vulnerabilities early, and closing security gaps before deployment. From design to deployment, agentic AI governance should span the entire adoption lifecycle, ensuring that agentic systems operate securely, ethically, and consistently deliver positive outcomes at scale. Without these guardrails in place, the risk of operational disruption and compliance failure increases significantly, potentially leading to severe financial, reputational and legal ramifications for your organisation. In fact, 55% of IT security leaders report lacking confidence in their current setup to enforce appropriate guardrails for deploying agentic AI solutions, while 79% are grappling with underlying compliance challenges.

A Roadmap for Strengthening Agentic AI Governance

Trust and reliability are instrumental to the successful and scalable implementation of agentic AI solutions. Yet declining consumer confidence raises critical concerns for organisations across both the public and private sectors. 57% of IT security leaders lack confidence in the accuracy or explainability of their agentic AI outputs. Additionally, 60% do not provide complete transparency around how customer data is used in agentic AI systems, and 59% are yet to establish mature guidelines for responsible usage. This highlights a clear gulf in readiness, underscoring the need for stronger governance, transparency and accountability before AI agents can be deployed with confidence, clarity, and compliance.

There is no one-size-fits-all solution for agentic AI governance, as each organisation faces distinct challenges shaped by its operational, technological, and regulatory environment. A customised, pragmatic, and consultative approach is essential for long-term success. Below are four proven best practices to strengthen agentic AI security mechanisms and governance frameworks across your enterprise.

1. AI Risk Maturity Assessment and Gap Analysis

Before integrating AI agents into your operations, it is crucial to conduct a thorough gap analysis between your current and desired future state. Start with an objective evaluation of your organisation’s current AI risk posture. Conducting an AI risk maturity assessment and gap analysis helps identify vulnerabilities, compliance gaps, and readiness levels, laying the foundation for a tailored governance roadmap. This roadmap serves as your guide, providing invaluable insights for a pragmatic and effective approach to piloting and scaling your agentic AI solutions.

Risk maturity assessments help you understand the necessary improvements in data quality and management, the upgrades needed for your existing architecture, and the optimisation of infrastructure for robust agentic AI governance. Additionally, they highlight the level of skills, competencies, and hard knowledge your employees have and the trainings they need to bridge the gaps. Once the evaluation is complete and all relevant stakeholders have been consulted, you can develop a robust action plan with a clear scope, time-bound deliverables, owners and contributors, and contingency plans. This proactive approach ensures a successful agentic AI implementation by anticipating and addressing potential challenges from the outset.

2. Prevent Agentic AI Sprawl with Orchestration

Without orchestration, AI agents can proliferate in silos, leading to inefficiencies and increased risk. Implementing orchestration frameworks ensures alignment, visibility, and control across AI deployments, enabling consistent performance and policy enforcement. This centralised approach prevents scattered usage and lack of uniformity, particularly in complex multi-agent systems. While customisation is encouraged across several functions and use cases, it is essential to maintain a clear and uniform framework for AI agent usage, deployment, and upgrades to avoid inconsistencies and potential violations.

A vital cog in agentic AI orchestration is ensuring that these AI agents operate within defined parameters and do not cause errors or decision paralysis. Clear levels of accountability and control are necessary to ensure safe, reliable, and responsible adoption. This approach boosts compliance and reduces potential loopholes in your agentic AI ecosystem, enhancing its accessibility and ease of interaction with the rest of your workforce.


3. Establish Mechanisms to Bolster Data Privacy and Cybersecurity

Agentic AI systems rely on high-quality and credible data, which must be protected. This strategy focuses on embedding enterprise-grade security, privacy protocols, and access controls into every layer of your AI architecture to mitigate threats and ensure compliance. Issues such as trust, bias, and hallucinations can lead to inaccurate results and expose you to cyberattacks and threats. It is essential to safeguard against malicious forces and implement relevant security measures.

As 75% of organisations plan to increase their security budgets in the coming year, data privacy and information security must remain top priorities in agentic AI adoption, with all users and stakeholders trained to use these solutions safely and securely. When AI agents interact with customers and third parties, it is critical to ensure vigilance and compliance with local and global regulations and legislative policies. Adopting a proactive approach to elevating data privacy and securing your agentic AI architecture is mission-critical.

4. Foster User Trust Through Comprehensive Guidelines and Trainings

Agentic AI governance goes beyond just technology; it is a cohesion of systems, culture, and people. Equip your teams with clear AI usage guidelines, ethical standards, and training programmes to promote responsible AI adoption and build trust in human-AI collaboration. Effective interaction, training, communication, and management of AI agents are complex tasks that require tailored workshops and capacity-building touchpoints for employees across different functions, technical expertise, and and experience levels.

As part of boosting agentic AI literacy, focus on implementing guidelines and policy documents while explaining the purpose behind these changes. Employee buy-in, accountability, trust, and transparency are fundamental to scaling your agentic AI solutions effectively. Do not overlook the human element in your AI setup; ensuring cohesion between people and AI is pivotal for successful, secure, and sustainable implementation.