AI Is Supercharging Work...and Your Attack Surface

This guest blog was contributed by Ben Henry, Field CTO at Komprise

AI is now embedded in day-to-day work. Individuals and teams are using the technology to summarize reports, write emails, analyze data and accelerate the efficiency and automation of nearly every task. In the corporate world, productivity is rising, but so is risk. The AI tools themselves admittedly make mistakes, skewing results and sometimes delivering incorrect information and hallucinations. There is also a real issue with corporate governance and compliance: a lack of controls, policies and visibility around employee usage and data flows into AI. 

Very few organizations these days restrict which AI tools employees can use. Yet IT leaders remain concerned about data leakage and PII exposure to AI as top fears. Both findings were uncovered in the Komprise 2026 State of Unstructured Data Management report. Organizations want the benefits of AI and are investing in the technology, but they haven’t secured the foundation needed to use AI safely across the enterprise. This leads to potential regulatory fines and reputational damage. According to research from IBM and the Ponemon Institute, the average cost of handling one data breach incident is $4.4 million. 

Shadow AI and the Unseen Exposure Problem 

Shadow AI, which is when employees use AI tools without oversight and approval, is a major new risk. Workers often paste sensitive data into public models because they’re fast and convenient, but every prompt containing proprietary, sensitive or regulated information creates exposure that can’t be undone.  

Accidental leaks are as simple as an individual uploading a confidential memo, intellectual property such as code or a customer list into a public tool. Intentional misuse occurs when employees try to bypass security controls. Generative AI (GenAI) compounds the danger because data may be logged, retained, or used to train models once it leaves your environment. Organizations need more than policies; they need infrastructure that prevents exposure in the first place. 

The Link Between AI and Ransomware Risk  

AI risk and ransomware risk may appear unrelated, but they are tightly intertwined. Both can be symptoms of the same underlying challenge: sprawling, ungoverned, unstructured data. 

When employees freely use AI tools, they generate more drafts, more versions and more copies of files across desktops, cloud drives, email threads and collaboration apps. Sensitive data multiplies and spreads, often without proper classification or security. This expands the potential attack surface for cybercriminals. 

Ransomware groups deliberately target this unstructured data because it is often improperly permissioned, inconsistently backed up, and rich with exploitable information. If attackers steal or encrypt sensitive files that have also been exposed to AI systems, the organization faces compounded damage. Now they must manage both a potential external data breach via AI tools and a direct ransomware attack inside their network.  

How Weak Data Governance Turns Into a Cyber Disaster 

It only takes one mistake to kick off a chain reaction: 

1. An employee uploads a document containing customer birthdates and addresses into a generative AI tool to “clean up the writing.” 

2. That same document sits on a shared drive with overly broad access controls. 

3. Attackers breach the environment and exfiltrate or encrypt the file during a ransomware attempt. 

4. The company must now respond to two risks at once: the internal breach and the untracked external exposure. 

This is how small errors turn into major incidents. Data that should have been restricted instead becomes the spark that ignites a broader crisis. 

What Tough Cyber Resilience Requires in the Age of AI 

To embrace AI safely, you need a stronger, data-centric cybersecurity foundation. Written policies alone are not enough. The technical safeguards must be able to detect and document data exposure wherever it occurs. Here’s what to do:

• Insights across all storage systems: IT needs a single pane of glass to view data across all locations and see who can access it and quickly identify risks such as misplaced PII, excessive duplicate and orphaned data or anomalies by department or owner.  

• Deep search and tagging capabilities: PII and sensitive information must be identified accurately. This requires metadata analysis, pattern detection, and automated tagging so that high-risk files are always flagged. 

• Workflow automation to prevent bad ingestion: Automation should remove sensitive data from AI ingestion pipelines, block uploads where necessary and route high-risk content to secure locations. Using data management solutions to automate feeding contextual data to RAG so employees don’t have to do this manually reduces security risk. 

• Granular auditing of AI usage and outcomes: Organizations need detailed logs showing which employees used which AI tools, what data was involved and the outputs. This traceability is critical when investigating incidents or proving compliance.  Again, when using a data management solution to ingest data into AI, the solution keeps track of what data was sent, to where and when. This provides an audit trail for data governance.  

• Clear AI usage policies: Approved tools, restricted data types and protocols for safe usage must be documented and reinforced through training and technical enforcement. 

• Reduction of the attack surface: Take time to archive or delete duplicate or unnecessary files. Smaller data footprints are easier to protect and harder for attackers to exploit.  You can shrink the active attack surface by tiering and offloading cold data away from the exposed file storage. 

• Immutable backups and isolated recovery stores: Clean, unchangeable versions of critical data must be preserved to ensure fast, reliable recovery after an attack. 

• Continuous monitoring and anomaly detection: Watch for suspicious file behavior, unauthorized access and early signs of encryption attempts. 

The C-Suite AI Data Governance Mandate 

Executives must recognize that AI adoption and cyber risk have merged. The speed at which employees can generate or expose data has outpaced traditional security practices. To avoid serious vulnerabilities and considerable financial losses, IT needs unstructured data visibility across disparate data storage silos, plus the ability to automatically tag files and audit data workflows. A modern AI strategy is inseparable from a modern cybersecurity strategy. You cannot have one without the other.