Find Your Security Gaps Before AI Does
Before AI Finds Your Forgotten Data, Find the Security Gaps Around It – AI does not need to bypass security when outdated permissions already provide a path.
Ask an enterprise AI assistant to summarize what your organization knows about a customer, project, employee, or acquisition. Within seconds, it may surface years-old files, inactive collaboration spaces, past emails and chats, and documents that were shared more broadly than anyone remembers.
No system had to be hacked. The AI may simply be using access already granted to the person asking the question.
That is what makes AI-related data exposure different. It does not always depend on a new vulnerability or malicious behavior. In many cases, AI simply removes the time and effort that once kept forgotten information difficult to find.
Permission-aware is not the same as permission-correct.
SC Media recently described how data sprawl, orphaned content, and accumulated access can become far more consequential when AI can search and assemble information at machine speed. The risk is not that AI suddenly creates sensitive data. It is that AI makes existing exposure visible, useful, and easy to redistribute.
The Risk Was Already There
Data sprawl rarely comes from one bad decision. It develops through normal business activity. Teams create folders, launch projects, share files, change roles, add contractors, and adopt new applications. The work moves forward, but access and ownership do not always get cleaned up behind it.
- Stale or inactive user accounts
- Former employees, contractors, or service accounts that retain access
- Abandoned collaboration sites, shared drives, and applications
- Excessive or outdated permissions
- Devices and systems that lack expected security controls
- Known risks that were documented but never fully remediated
For years, some of this exposure remained unnoticed because finding the right information required knowing where to look. AI changes that. It can search broadly, connect information across sources, and return a polished answer without the user ever opening each underlying file.
AI Turns Hidden Exposure Into Usable Information
Microsoft explains that Microsoft 365 Copilot operates within existing permissions and access controls. That is an important safeguard, but it also means overshared or poorly governed content can influence what Copilot returns.
Microsoft 365 Copilot security guidance
A user may technically be authorized to open an old finance folder, a former project site, or a customer file. That does not mean the access is still appropriate. AI compresses the discovery process from hours of manual searching into seconds. It can then combine the results into a response that may be copied into an email, presentation, ticket, or another AI prompt.
The security challenge is therefore larger than controlling the AI tool. Organizations also need to correct the identities, assets, permissions, and security gaps that determine what the tool can reach.
Data Security and ASM Solve Different Parts of the Problem
Data classification, Data Loss Prevention, Data Security Posture Management, and AI governance solutions are essential for identifying sensitive information, monitoring AI interactions, and controlling how data can be used or shared.
Attack Surface Management (ASM) addresses a different layer: the users, devices, systems, configurations, and security controls surrounding that data.
Together, these capabilities help answer two distinct questions:
- Data security: What information is sensitive, where is it stored, and how may it be shared?
- Inside-out ASM: Which users, devices, systems, and control gaps expand the paths to that information?
Sedara ASM does not replace DLP, data classification, or AI monitoring. It helps organizations expose and remediate the environmental conditions that make AI-related data exposure more likely and more difficult to control.
What Inside-Out ASM Contributes
Sedara ASM brings information from identity platforms, endpoint tools, device-management systems, security products, vulnerability scanners, and other connected data sources into a unified view of the internal attack surface.
This gives security and IT teams the context needed to move from a broad concern about AI risk to specific exposures that can be assigned, remediated, and monitored.
1. Establish a Reliable Inventory
Organizations cannot govern AI access using an incomplete picture of their environment. ASM helps establish a more complete inventory of users, devices, systems, and security-tool coverage.
That foundation can reveal unmanaged assets, duplicate records, incomplete integrations, and systems with unclear ownership.
2. Expose Identity and Control Gaps
Inactive accounts, outdated privileges, and poorly maintained account lifecycles create unnecessary access paths. The risk becomes greater when the associated devices are missing endpoint protection, running unsupported software, operating in bypass mode, or failing expected control requirements.
By correlating identity, asset, and security data, ASM helps teams identify where access and protection no longer align.
3. Prioritize Risk With Context
Not every stale account or missing control creates the same business risk. An inactive privileged identity connected to sensitive systems requires different attention than a low-impact issue on an isolated asset.
Sedara ASM helps teams prioritize remediation using asset criticality, business impact, available controls, and the context surrounding the exposure.
4. Turn Findings Into Accountable Remediation
A dashboard does not reduce risk by itself. Teams need clear ownership, practical instructions, and a way to document what has or has not been resolved.
Contextual playbooks and guided remediation help teams take action. The Risk Register provides a structured way to track mitigated, accepted, transferred, or unresolved risk.
5. Monitor and Demonstrate Improvement
AI readiness is not a one-time cleanup project. Users change roles, devices are replaced, applications are introduced, and permissions continue to evolve.
Ongoing monitoring helps identify when resolved conditions return, while exposure trends and remediation progress help leaders demonstrate whether risk is actually being reduced.
What Security Teams Should Do Before Expanding AI
Microsoft recommends addressing high-risk sites, files, oversharing, and access issues as part of building a secure foundation for Microsoft 365 Copilot.
The NIST AI Risk Management Framework similarly encourages organizations to govern, map, measure, and manage AI-related risk rather than treating AI security as a single control or product decision.
NIST AI Risk Management Framework
Before expanding AI access, organizations should strengthen both the data layer and the environment around it:
- Identify the sensitive information AI may reach and where it is stored
- Review access, sharing, and ownership for high-risk data sources
- Remove stale accounts, unnecessary privileges, and abandoned access paths
- Verify that connected devices and systems have the required security controls
- Assign remediation owners and track unresolved risk
- Monitor for new exposures as identities, assets, applications, and AI use change
The goal is not to eliminate AI. It is to give the organization enough visibility and control to adopt AI without allowing years of accumulated security debt to define the outcome.
Find the Gaps Before AI Does
AI can deliver enormous value, but organizations cannot govern its access using assumptions about where information should live or who should still be able to reach it.
Sedara ASM helps uncover the internal users, assets, control gaps, and unresolved risks surrounding organizational information. By combining visibility, contextual prioritization, guided remediation, risk tracking, and ongoing monitoring, Sedara helps teams build a stronger foundation for responsible AI adoption.
AI did not create years of accumulated security debt. It is making that debt easier to find and harder to ignore.
Start with a focused Sedara ASM assessment to identify the users, assets, and security gaps that could expand AI-related exposure.