Rapid adoption of AI technology is a crucial business initiative. In fact, 82 percent of executives at large enterprises plan to integrate AI agents within the next three years. As organizations race to adopt agentic AI, many overlook a critical factor: security.
Rushing into deployment without proper safeguards can introduce serious risks: from exposing sensitive data and violating regulatory compliance requirements to creating untraceable Non-Human Identities (NHIs) and attack surfaces that span across SaaS tools, internal systems, and autonomous agents.
This AI Adoption Security Guide presents a maturity model that helps enterprises navigate the evolution of agentic AI integration—step by step—while embedding security, governance, and risk mitigation into every phase.
What’s In It For Me?
Navigating Enterprise AI Adoption
AI Maturity Model Phases
Introduction: Navigating the Maturity of Secure Agentic AI Adoption
As enterprises race to harness the power of generative and autonomous AI, security and governance must evolve in parallel.
Agentic AI, which are AI systems capable of making decisions, taking actions, and operating independently across environments, offers unprecedented opportunities for innovation, efficiency, and scale. However, without a structured approach, these capabilities can introduce significant risks, including data exposure, identity sprawl, compliance violations, and untraceable decision-making.
Research from ISACA found that 81% of employees are using AI in the workplace without formal policies in place, and only 28% of organizations have formal AI governance frameworks, highlighting how identity‑ and access‑centric oversight is critical before scaling agentic systems securely.
This maturity model provides a phased framework to help organizations adopt Agentic AI securely and responsibly. It outlines the progression from ad-hoc experimentation to fully governed, autonomous AI operations while highlighting key challenges, risks, identity and access management (IAM) gaps, and recommended controls at each stage.
By aligning security with each phase of AI evolution, enterprises can foster innovation while maintaining visibility, trust, and control. The model empowers IT, security, and business leaders to build scalable AI infrastructure, operationalize AI safely, and unlock transformative outcomes to stay ahead of emerging threats and compliance demands.
Throughout each stage, the emphasis on non-human identity (NHI) security, data governance, model integrity, and third-party risk management creates a foundation for responsible AI adoption.
The positive business outcomes are significant:
Accelerated innovation and productivity
Reduced operational latency through automation
Increased trust in AI-driven decisions
Stronger compliance and audit readiness
Competitive differentiation through proprietary AI systems
By following this maturity model, enterprises can confidently scale Agentic AI initiatives and transform how work gets done while keeping security and governance at the core.
Token Security secures Non-Human Identities across cloud services, CI/CD pipelines, and Agentic AI. Its agentless, AI-native platform provides complete visibility, lifecycle and security posture management, and real-time threat detection. Token Security enables security teams to reduce risk, automate remediation, and accelerate innovation
Descope is a no / low code platform that helps hundreds of organizations manage identity journeys for their customers, partners, and AI agents. AI developers use Descope to secure their APIs, remote MCP servers, and AI agents with authentication, granular authorization, user consent, and token management.
