How AI can reshape SOC reviews

SOC reports are still critical for evaluating internal control effectiveness, especially as organizations rely more and more on third-party service providers. These reports have grown in volume and complexity, but improving the traditional process of manually reviewing them has not kept pace. Audit and risk teams are expected to increase efficiency anyway, making traditional time-intensive reviews even less useful.

Artificial intelligence (AI) can solve this problem. It can help modernize SOC report review process in four key ways: control identification through risk alignment, trend analysis, operational efficiency, and governance – all without compromising professional judgement. This article explores these four opportunities in detail.

Background: the evolving SOC review landscape

The SOC report review process, which is critical for third-party risk management and assuring adequate internal control coverage, must perpetually evolve to stay useful. It’s common for large organizations today to manage significant third-party networks with multiple and long-standing service provider relationships. This relationship continuity is efficient but also risky if SOC report reviews become repetitive and overly focused on form over substance. Organizations must annually evaluate these providers’ relevance and scope.

Meanwhile, expectations from auditors, regulators and internal stakeholders continue to rise. There’s greater emphasis on understanding how third-party control environments change over time, how risks are evolving, and whether control coverage remains appropriate. Due to these pressures, many organizations are reconsidering how they perform SOC report reviews and where technology — particularly AI — can improve outcomes.

Enhancing control identification through risk alignment

One of the most practical ways of applying AI to SOC report reviews is in control identification and mapping. SOC reports often contain extensive narrative descriptions of systems, processes and controls, requiring reviewers to manually search for relevant controls and determine their applicability. Often, this part of the review can become repetitive. It’s common to observe organizations no longer addressing certain controls and their associated risks because the same old methodologies are blindly reapplied.

AI can automate this by analyzing SOC report content to identify control descriptions, then pair those controls with an organization’s risk and control matrix (RCM). By aligning identified controls to defined risks, audit and risk teams can more quickly determine which controls are relevant, which risks are addressed, and where potential gaps or redundancies exist.

This approach does not replace professional judgment. Instead, it speeds up the initial identification and mapping process, so reviewers can just focus on evaluating control design, operating effectiveness and overall risk coverage. When consistently applied, AI-supported control mapping also promotes greater standardization across reviews, especially in organizations utilizing large volumes of SOC reports.

Using AI-enabled trend analysis to improve reviews

Recurring SOC report reviews present a unique opportunity to move beyond static, point-in-time assessments. AI can support trend analysis by comparing current and prior-year SOC reports to identify anomalies, new items or meaningful control environment shifts, instead of re-reviewing the same information each year.

AI can analyze trends to show:

• New or modified controls introduced by the service organization
• Changes in system descriptions or control responsibilities
• Emerging themes in complementary user entity controls
• Control deficiencies that have the potential to materially impact the user organization or its control environment
• Deviations from previously established control patterns

By surfacing these items early, audit and risk teams can focus their efforts on areas that warrant deeper analysis, while deprioritizing sections that remain stable year over year. This risk-focused approach improves both efficiency and effectiveness, ensuring project time is spent where it adds the most value.

Driving efficiency without sacrificing quality

AI can deliver meaningful efficiency gains when applied across recurring SOC report review cycles. Tasks that are traditionally time-consuming, such as reviewing lengthy reports, extracting control language, identifying anomalies, and performing initial relevance assessments, can be significantly streamlined.

These efficiencies translate into:

• Reduced manual effort and review fatigue
• Faster annual SOC report review completion
• More consistent application of review criteria Improved documentation quality and audit traceability

Importantly, AI enables teams to reallocate time toward higher-value activities, such as engaging with stakeholders, investigating exceptions, and evaluating the broader impact of third-party risks on the organization.

Maintaining governance and professional judgment

While AI’s benefits are compelling, successful adoption requires appropriate governance. The outputs generated by AI tools must be transparent, explainable, and subject to human review. Data quality remains critical, as incomplete or poorly structured SOC reports can affect results and delay project milestones.

Organizations should also clearly define where AI supports the audit process and what requires professional judgment. Decisions related to risk significance, control sufficiency, and remediation priorities should always remain with experienced audit and risk professionals.

What’s next

AI’s role in SOC report reviews will likely expand as its capabilities continue to mature. Future applications may include more continuous monitoring of third-party risks, deeper integration with enterprise risk management and compliance frameworks, and enhanced analytics across multiple service providers.

Organizations that thoughtfully incorporate AI into their SOC report review process — especially around control identification, risk alignment and trend analysis — can better enhance insight and strengthen overall third-party risk management.

How we can help

Sikich uses AI to make SOC report reviews and third-party risk management more efficient and consistent. By combining AI-driven analytics with audit, risk, and compliance expertise, Sikich helps organizations get clearer, more useful insights from SOC reports.

Sikich applies AI across key parts of the SOC report review process, including aligning SOC reports to risk and control matrices, researching noted control deficiencies, and analyzing year-over-year trends to spot anomalies and emerging risks. These capabilities support recurring SOC report reviews and keep focus on the control areas that most impact the business. To learn how Sikich can support your SOC report review and third-party risk management needs, contact a Sikich professional.