AI Copilots and Agentic AI (those capable of independently taking actions to achieve specified goals) remain the talk of the town. While no longer confined to research papers or speculative fiction, they have yet to become transformative realities for organizations. The pressure is on though, as every organization is eager to harness its potential and become an AI-powered version of itself. However in the copilot adoption race, an unfortunate amount of organizations are being burnt by their exuberance, and lack of clear objectives or understanding the necessary groundwork. Employees everywhere are testing and experimenting with AI tools en masse.
For security and privacy professionals, this pressure to adopt AI at scale and rampant experimentation represents both unprecedented opportunity and sobering responsibility. As guardians of an organization’s most valuable asset—its data—we find ourselves at a critical inflection point. The trustworthiness of the data and ability to secure and control its use can guide organizations past potential breaches, compliance violations, and ethical dilemmas.
This blog draws from experiences of our customers and how they have leveraged the greater insight and control of data we provide to navigate through Copilot Adoption and now on to Agentic AI. In it, we outline five essential strategies for preparing your data estate for the era of agentic AI, with particular emphasis on protecting sensitive information and maintaining robust data security postures.
Establish a Complete Data and Identity Inventory
Before adopting AI, organizations need a clear understanding of their data assets. This means identifying where data is stored, its sensitivity, who can access it, and the business context behind it. Data discovery should cover all repositories, including cloud storage, on-premises databases, SaaS platforms, and shadow IT resources. Organizations must know exactly who or what has access to each dataset to prevent excessive permissions that could expose sensitive data to AI systems. Classifying data based on regulatory requirements and business value helps define what AI systems can and cannot access. Understanding the business context for each dataset ensures AI systems use the data correctly and align with business goals. With this foundation, organizations can set up the guardrails needed to guide AI systems responsibly.
Strengthen Data Governance and Access Controls
AI makes implementing least privilege access more complex but also more critical. Forward-thinking organizations are creating dedicated data environments and pipelines for AI, limiting access to only the data necessary for AI operations. Access levels should be based on roles and responsibilities, covering both human and machine identities. Strong classification frameworks should prevent AI from accessing or sharing sensitive data improperly. Consistent naming conventions and data retention policies help manage the data lifecycle. Dynamic permission frameworks are key for agentic AI systems, adjusting access based on the system’s current task and operational context while always defaulting to the most restrictive setting.
Consolidate and Organize Data Sources
Consolidating data starts with cataloging all organizational data assets, but organizations need more than in a world where data sprawl means petabytes of duplicate data is created across enterprises every day. Organizations should have capabilities to identify redundant, obsolete and trivial data and remediate this data as appropriate.
Monitoring Data Quality and Integrity
Poor data quality and integrity can lead AI to make bad decisions, causing regulatory and reputational damage, but is often treated as a third class citizen in the CIA triad. Organizations need to treat Data quality and integrity as a security priority in the world of agentic AI. This begins with controlling who can modify your data. Organizations need clear access frameworks that match responsibilities with modification privileges and require approvals for significant changes. By limiting data alteration to authorized personnel with proper expertise, you establish the first line of defense against data corruption.
Once access controls are in place, comprehensive monitoring becomes essential. Changes to AI datasets should be tracked through detailed audit trails capturing who made modifications, when, and why. These monitoring systems should maintain version histories and alert teams to unusual patterns, providing complete visibility into how your data evolves over time.
The final component focuses on ensuring data quality through automated verification. AI-driven monitoring systems continuously check data integrity, detecting anomalies before they affect decisions, while cryptographic validation authenticates sources throughout the data lifecycle. When conflicts arise, formal reconciliation processes establish authoritative sources, preventing contradictory information from compromising AI outcomes. This three-part approach—controlling access, monitoring changes, and verifying quality—creates a foundation for trustworthy artificial intelligence.
Automate Data Management
AI operates at machine speed, so data management and data security needs to keep up. Organizations need to have capabilities to autonomously discover, classify, catalog, and enrich metadata about the data across the data estate.
Organizations should be aiming for near-real-time policy enforcement to evaluate each data access request against established rules before granting permission. Separately the ability to perform anomaly detection is essential to identify unusual access patterns in real-time, allowing immediate intervention to prevent security incidents.
The Path Forward: A Data Centric and Identity-First Approach to Copilot Adoption and Agentic AI
Security and privacy professionals have a unique opportunity to shape how agentic AI evolves within their organizations. They must architect security and privacy directly into the foundations of agentic infrastructure by engaging early in the development process and advocating for data estates designed with security as a first principle. It is necessary to build cross-functional teams that bring together data scientists, engineers, ethicists, and security professionals to address the multidimensional challenges agentic systems present. Traditional security models built around human users will not suffice in this new era. Agentic systems operate differently—with greater persistence, speed, and reach than their human counterparts. Our security and privacy frameworks must evolve accordingly.
Organizations that will successfully navigate this transition are those that view their data estates as critical infrastructure requiring systematic protection, governance, and ethical oversight. By implementing the five strategies outlined above, security and privacy professionals can help their organizations harness the transformative potential of agentic AI while mitigating its inherent risks.
