Key takeaways
- AI can take some of the manual work out of reviewing long SOC1 reports, but human judgment still matters.
- A standardized review process makes it easier to find important information and apply the same approach across multiple reports.
- Human review is needed both to confirm the AI has interpreted the report correctly and to determine what the information means.
- AI may also help organizations compare complementary user-entity controls with their own risk and control matrices.
Reviewing SOC1 reports is an important part of understanding the controls in place at third-party service providers, particularly when those providers support systems or processes that affect financial reporting.
However, it can also be a very time-consuming exercise.
A SOC report could be 50 pages long, or it might be closer to 200. Although SOC reports follow the same standards, they don’t all organize the information in the same way. The individuals responsible for reviewing SOC reports will often spend a significant amount of time simply finding the relevant sections, pulling out key details, and transferring them into a format that can be assessed consistently.
That is the kind of task where AI has real potential to help.
At Behunin & Associates, we’re developing an AI-assisted process that extracts key information from SOC reports and organizes it into a standardized review template. The goal is not to automate the reviewer’s judgment, but to reduce the laborious, repetitive work that happens before that judgment can be applied.
Why does a SOC report review take so much time?
Receiving a SOC report is only the first step, to complete a SOC report review, the reviewer may need to identify and analyze:
- The type of report and the period it covers
- The service auditor’s opinion
- Testing exceptions, and Management’s responses to those exceptions
- Subservice organizations included in the process
- Complementary user-entity controls (CUECs) and Management’s procedures to support these CUECs
- Gaps between the report period and the organization’s reporting date
The information is usually there, but finding it isn’t always straightforward. One report may present exceptions in a clear table, while another discusses them across several sections. Similar concepts may also be described using different terminology.
When an organization has only a few reports, a manual review process may be manageable. When it has dozens, the amount of manual work increases quickly.
At scale, even small improvements in how information is located and organized can make a meaningful difference.
Where can AI help with SOC reports?
The first opportunity is extraction.
Instead of asking a reviewer to begin every assessment by searching through the entire document, an AI-assisted process can look for the information the reviewer is likely to need and place it into a consistent template, so that they start with an organized summary rather than a blank document
This doesn’t eliminate the need to refer back to the original report. It does, however, reduce the amount of time spent searching for information and manually reorganizing it before the real analysis begins.
Standardization also helps when several people are involved in the review process. If everyone works from the same template and follows the same review structure, it becomes easier to apply a consistent approach across reports from different service providers.
Why we’re not using AI to make the final decision
Finding information and evaluating it are two different things.
AI may identify an exception, but it cannot independently determine whether that exception creates a meaningful risk for a particular organization. That depends on the service being provided, the systems involved, the financial reporting processes affected, and the controls the organization already has in place.
A qualified reviewer still needs to ask questions such as:
- Did the AI extract the information correctly?
- Is the exception relevant to the services our organization uses?
- Does it affect a key process or system?
- Has management’s response adequately addressed the issue?
- Do we have other controls that reduce the risk?
- Is additional follow-up or remediation required?
The first layer of human review confirms that the information has been captured accurately. The second applies professional judgment to determine what the information means.
That distinction matters. The value of AI in this process is not that it reaches the conclusion for the reviewer, but that it helps the reviewer reach the important part of the work more efficiently.
What about complementary user-entity controls?
Complementary user-entity controls (CUECs) are one of the most important parts of a SOC1 report review.
These controls describe activities the service organization expects its customer to perform. The service provider may have strong controls over its own environment, but the overall process may still depend on the customer reviewing access, reconciling information, monitoring transactions, or completing another control internally.
It’s not enough to identify those requirements in the SOC report. The organization also needs to determine whether it has corresponding controls in place.
We’re developing an AI-assisted process to compare the complementary user-entity controls listed in a SOC report with the controls in the client’s risk and control matrix.
The process helps to identify:
- A likely match with an existing control
- A requirement that needs further investigation
- A potential gap where no corresponding control is apparent
A reviewer needs to assess whether the controls are genuinely aligned and whether they operate as required. Even so, using AI to narrow the field can help teams focus their attention where it is most needed.
Where do we go from here?
AI is not going to remove the need for SOC report expertise. It may, however, change how experienced reviewers spend their time.
Instead of manually searching through every page for recurring information, reviewers can focus more of their attention on exceptions, control gaps, and the areas that require judgment.
That’s where we see the greatest opportunity: not replacing the reviewer, but giving the reviewer a better starting point.
As AI tools continue to improve, the challenge will be finding the right balance between efficiency and oversight. For SOC report analysis, that means using technology to handle repetitive extraction and comparison work while keeping experienced professionals responsible for validation, context, and the final assessment.
To find out more about how we’re using AI to streamline our processes, get in touch with the Behunin team.
Frequently Asked Questions
Can AI complete a SOC report review without human involvement?
No. AI can help extract and organize information, but a qualified reviewer still needs to validate the output and determine what the findings mean for the organization.
What information can an AI-assisted review identify?
The process may identify details such as the report period, auditor’s opinion, testing exceptions, management responses, subservice organizations, and complementary user-entity controls.
Can AI determine whether a control exception is significant?
It can flag the exception, but significance depends on the client’s systems, processes, risks, and existing controls. A reviewer must make that assessment.
How can AI help with complementary user-entity controls?
AI can compare the controls listed in the SOC report with the organization’s risk and control matrix, helping identify possible matches, items requiring further review, and potential gaps.
Does AI replace the need to read the original SOC report?
No. The original report remains the source document, and reviewers should refer back to it when validating extracted information and reaching their conclusions.