Manage AI data risk with IBM Guardium Exposure Manager
Announcing the launch of IBM Guardium Exposure Manager to allow organizations to gain visibility and control over enterprise data used across AI.
IBM Guardium Exposure Manager delivers end-to-end visibility and control over enterprise data used across AI agents, AI applications, databases and endpoints to allow organizations to secure data flowing through AI.
It helps security teams see how sensitive data moves across employee workflows and AI systems, detect exposure risks such as risky sharing attempts, AI-generated sensitive content and policy violations, and investigate incidents with lineage and context to understand where the data came from, who handled it, how it changed and where it went.
Generative AI and agentic AI are quickly becoming part of everyday enterprise workflows.
While this creates a major opportunity for productivity and innovation, it also creates a new data security challenge. Sensitive enterprise data is no longer moving only through familiar paths such as databases, applications, files,and endpoints. It is now flowing through employees, AI agents, AI applications, prompts, retrieval pipelines, generated content and sharing workflows.
That means security leaders need to ask a new set of questions:
- How does sensitive data enter AI systems?
- How is it transformed into AI-generated outputs?
- Where does it move next, and who handled it?
For many organizations, the answer is not yet clear.
In a recent Omdia report sponsored by IBM, 31% of organizations have experienced AI-related cybersecurity incidents from data privacy violations, including AI accessing, sharing or exposing sensitive data without proper safeguards. The same research found that 30% cite data exposure from inadequate protection of data used in AI models as a top concern related to agentic AI.
Additionally, the 2026 Cost of Data Breach Report found that the average data breach costs USD 4.88 million and 97% of organizations that reported an AI-related security incident lacked proper AI access controls. Even more concerning, incidents involving unapproved AI tools more than doubled to 43% of organizations in 2026, with these breaches costing an average of USD 5.39 million. They also had serious consequences, including data loss or compromise (49%) and disrupted operations (42%).
These findings point to a critical shift: AI security is not only about protecting models. It is also about protecting the enterprise data that flows into, through and out of AI systems.
As AI adoption accelerates, organizations need to rethink how they secure enterprise data across both employee workflows and AI systems. Sensitive data can now move from source systems into employee downloads, AI tools, RAG pipelines, model outputs and derivative files.
There are three best practices:
1. Secure employee AI data
A first best practice is to secure employee AI data use by understanding how employees’ access, upload, copy, paste, share or transform sensitive data through AI. This helps organizations reduce exposure risk by identifying sensitive data movement across AI workflows before it becomes a larger security or compliance issue.
2. Govern the data used in AI systems
Organizations then need visibility into what enterprise data is connected to AI applications, RAG pipelines, vector databases and AI-generated responses. This requires more than discovering where sensitive data lives; it requires understanding how that data is being used, whether it is policy-aligned and whether there is evidence to support governance decisions. By applying visibility, policy and evidence across AI data usage, organizations can strengthen governance while enabling AI teams to move forward with greater confidence.
3. Investigate AI data exposure with context
Finally, when risk appears, security teams need to quickly understand where the data came from, who handled it, how it changed and where it went. This context can help reduce investigation time, support audit readiness, and improve response across both workforce and workload. Together, these practices help organizations move from reactive AI risk management to a more proactive model for securing data as AI scales.
By bringing together visibility, policy, alerts and investigation context across workforce and workload interactions, Guardium Exposure Manager helps organizations reduce sensitive data exposure, strengthen AI governance and securely scale AI adoption.
This helps various teams working on AI initiatives within the organization:
- For CISOs, this means reducing exposure risk while enabling innovation.
- For Chief AI Officers, it means helping AI projects move forward with stronger governance.
- For Chief Technology Officers, it means innovating with confidence and trust.
- For risk and business leaders, it means scaling AI with greater confidence.
- For data security analysts, it means faster investigations with better context.
Employees are using AI tools to summarize documents, analyze customer data, generate reports, write code and accelerate decisions. At the same time, organizations are building AI applications and AI agents that connect to internal data sources through RAG pipelines, vector databases, model inference and AI-generated outputs.
Security teams have traditionally been seen as a “department of No.” but this is an opportunity for the security teams to enable trustworthy adoption of AI by making sure to put right data security safeguards in place. IBM Guardium Exposure Manager can help. It helps identify these data risks, strengthen AI governance and securely scale AI adoption.