Securing Data in the AI era

Data has always been critical to the business. In the AI era, it has become something more: the foundation for how AI applications learn, reason, and act. AI systems use it as context, retrieve it to answer questions, and increasingly act on it. As AI adoption accelerates, more applications and identities are connecting to sensitive data, including AI systems that can access, retrieve, and surface that data in ways that weren’t possible before. For security teams, the challenge isn't simply more data to protect. AI is reshaping how data is accessed, used, and exposed, and with it, what organizations need to understand to secure it. Data that appears appropriately protected in isolation may take on very different risks when an AI agent can access it, act on it, or expose it through an unexpected path. The fundamental mission of data security hasn't changed: know where sensitive data is, understand who and what can access it, and reduce its exposure. But in the AI era, answering those questions requires a much larger and more connected picture, and a different kind of context Securing data for the AI era starts with understanding that expanded responsibility. The expanding responsibility of data security The fundamental questions of data security haven't gone away. But AI is expanding what security teams need to ask. Where is my sensitive data? And where is it being used to train, ground, or power AI? Who has access to it? And which AI applications, agents, models, and identities can reach it? How is that data exposed? Could an AI system retrieve it, act on it, or unintentionally reveal it? What creates real risk? Is sensitive data connected to an agent with powerful capabilities, excessive permissions, or exposure to untrusted inputs? Consider a company that builds an AI agent to help employees research and respond to customer requests. To do its job, the agent can search the internet, execute code, and access customer data stored in the cloud through its workload identity. Every capability has a legitimate purpose. But together, they introduce a new path to sensitive data. If the agent retrieves malicious content from the internet, a hidden instruction could influence its behavior and use its existing capabilities and permissions to access and exfiltrate sensitive customer information. The risk to sensitive data is increasingly defined by what connects to it. Protecting it requires understanding the identities, infrastructure, AI systems, and capabilities that can reach it and shape how it is used. What does data security require in the AI era? As data risk becomes more connected, data security needs to evolve with it. Discovery and classification remain foundational, but organizations also need the context to determine where real exposure exists and the ability to act on it quickly. That requires three things: Context across every layer. Data now spans cloud, SaaS, and AI environments. Organizations need visibility across this expanding surface and a unified understanding of the data, identities, applications, and infrastructure involved. Validation and prioritization. Not every finding represents the same risk. Effective access, exposure, and surrounding security context help distinguish isolated findings from the combinations that can put sensitive data at risk. Faster Remediation. Understanding risk must translate into action. As environments and threats move faster, teams need to streamline remediation, automate repeatable responses, and make it easier for the right teams to fix issues quickly. At Wiz, this comes together around three outcomes: know your data, understand real risk, and reduce exposure while responding at speed. Let’s look at how each evolves for the AI era. 1. Know your data and everything connected to it Securing data starts with knowing where it lives, what it contains, and who or what can access it. That is already difficult across cloud, SaaS, data warehouses, and other modern data platforms. AI expands that visibility challenge. Data is increasingly used across vector databases, agents, and AI applications, creating new data stores and access paths to understand. Organizations also need to know which datasets are being used by AI and which AI systems and identities can reach sensitive data. Wiz brings this entire picture into view. Wiz continuously discovers and classifies sensitive data across cloud, SaaS, and data platforms, while identifying datasets used by AI and surfacing them as part of the broader data inventory. Agentless scanning provides broad coverage without requiring teams to deploy and manage agents across every data source. Wiz then connects that data to the identities, permissions, workloads, applications, and AI systems that can access it. For AI applications, Wiz discovers components such as agents, models, tools, and MCP servers and maps them to the underlying cloud resources and identities they rely on. In our customer support example, that connected visibility reveals the sensitive customer data, the agent that uses it, and the workload identity that gives the agent access. Instead of separate inventories, teams can see how data access flows across the environment. This creates a connected map of sensitive data and its access, including where data is powering AI and which AI systems can reach it. 2. Understand and validate where sensitive data is truly at risk Finding sensitive data is only the first step. Teams need to understand what the data is, how sensitive it is, and whether the surrounding context creates a meaningful path to it. That means combining accurate classification with effective access, identities, permissions, exposure, vulnerabilities, and other security context, then validating where risk is actually exploitable. Classify data with precision AI adds new complexity, but also new ways to solve it. Pattern matching remains valuable for recognizable data types, but AI-powered semantic analysis can understand the meaning and context of unstructured data that traditional rules and classification may miss. Most tools run pattern matching regex rules built to recognize credit card formats, Social Security numbers, known PII markers. They find what they were told to find. A credit card number in a live production database and the same pattern in a synthetic test dataset look identical. The risk is completely different. Wiz applies multiple engines simultaneously. Pattern matching for known data types. AI-powered metadata analysis that identifies sensitive clusters even when patterns are absent. Semantic analysis that reads the meaning of a document, so a W-2 form gets classified because the model understands what it is. Novel classifiers discover sensitive data categories that no one defined in advance, proprietary research, internal pricing models, engineering documents. The result is classification precise enough to trust. Fewer false positives. A signal clean enough to act on. Wiz then maps every finding to a sensitivity tier automatically, so the business impact of a finding is visible before any additional context is added. From sensitive data to real risk Understanding the data is only half of the equation. Wiz connects that context with effective access, identities, permissions, vulnerabilities, exposure, infrastructure, and AI application capabilities in the Wiz Security Graph. This surfaces toxic combinations where individual risks come together to create a meaningful path to sensitive data and, where applicable, validates whether that path is actually exploitable. Wiz Red Agent can take this a step further, actively probing the environment to validate whether a path is exploitable. This helps teams distinguish theoretical risk from a confirmed attack path. In our customer support example, that means connecting three critical signals: the agent processes untrusted internet content, it has powerful capabilities such as code execution, and its workload identity can access sensitive customer data. Together, Wiz can surface the path where an indirect prompt injection could ultimately lead to sensitive data exfiltration. Instead of treating those signals as disconnected findings, Wiz brings them together into a prioritized Issue that shows why the data is at risk and what makes that risk critical. 3. Reduce and respond to data exposure at speed Knowing where data is at risk is one thing. Fixing it before that exposure can be exploited is another. Because data exposure can stem from many different paths, response is not one-size-fits-all. The right action might be to remove public exposure, restrict access, change permissions, rotate a secret, fix code or infrastructure, or adjust the capabilities of an AI application. AI makes time to action even more critical. As AI accelerates development and gives attackers new ways to operate at machine speed, security teams need to close the gap between identifying and reducing risk. This is central to AI threat readiness: fixing needs to become as fast as breaking. Wiz turns context into the right action for each risk. Green Agent analyzes the full context behind an Issue to identify the root cause, determine the right owner, and recommend the most effective way to break the path. Teams can then choose the response that fits, from guided remediation and direct fixes to code or IaC changes, developer-led remediation, or fully automated response with Wiz Workflows. In our customer support example, those options could include removing unnecessary code execution capabilities, restricting the workload identity to only the customer data it needs, or adding protections against prompt injection. Teams can see exactly what needs to change to break the path to sensitive data. Wiz Workflows can operationalize those responses at scale, automatically triggering Green Agent, routing context-rich remediation to the right owner through tools like Jira or Slack, opening pull requests for code or IaC fixes, or executing supported remediation actions automatically. Teams can automate repeatable fixes while keeping human oversight where it matters. That shortens the path from identifying data exposure to reducing it, with the flexibility to take the right action for each risk at the speed AI demands. Putting data security into action A connected approach to data security gives every team the context they need to act, without requiring everyone to become a data security expert: Data security teams get a unified view of exposure and can set priorities across the organization. AI and application teams understand the data implications of what they build and can address risk earlier. Developers and resource owners get the context and ownership they need to fix issues in the workflows where they already work. Security teams can scale oversight and response, automating repeatable work while maintaining control where human judgment matters. With a shared understanding of data risk, the right context and action can reach the people best positioned to reduce it, without security becoming the bottleneck. Data security built for the AI era AI will continue expanding how sensitive data is accessed, used, and exposed. The organizations that stay ahead won't be the ones with the most classifiers. They'll be the ones that can quickly and confidently answer what's actually at risk, why it matters, and what to do about it. By bringing data, cloud, SaaS, and AI context together, Wiz helps teams do exactly that.