The initial phase of any audit – responding to the Information Request List (IRL) – often represents a significant operational burden for GRC teams. While seemingly straightforward, the process of locating, verifying, and contextualizing evidence for control operation is frequently characterized by manual effort, fragmented data sources, and potential for human error. This isn’t a failing of GRC professionals; it’s a systemic challenge inherent in traditional approaches.
Traditionally, responding to an IRL involves a resource-intensive “treasure hunt.” Analysts spend considerable time identifying relevant stakeholders, requesting documentation (often in disparate formats), validating its accuracy and completeness, and then manually correlating that evidence to the specific control, associated risk, and governing policy. This verification can involve manually cross-referencing data across systems, checking dates and signatures, and ensuring the evidence truly demonstrates effective control operation. Then comes the contextualization – linking that evidence back to the specific control, risk, and policy it addresses. This often means manually creating a traceability matrix. This manual correlation often results in fragile spreadsheets or hastily assembled reports, and potentially introducing inconsistencies. The inevitable clarifying questions then trigger another iteration of this process, extending timelines and increasing cost.
This manual process is a significant drain on resources – conservatively costing organizations tens of thousands of dollars per audit. It pulls GRC professionals away from strategic initiatives like proactive risk assessments and process improvement, burdens control owners with ad-hoc requests, and introduces the risk of errors, inconsistencies, and even non-compliance. It’s a reactive fire drill, not proactive GRC.
But what if that fire drill could be significantly minimized, even extinguished?
Leveraging AI GRC, specifically through technologies like a “Trust Graph,” offers a fundamentally different approach. The Trust Graph maps the relationships between all GRC artifacts – risks, policies, controls, standards, and all supporting evidence – creating a semantic understanding of your GRC program. This isn’t just tagging; it’s building a model of how your controls mitigate risks, how policies guide those controls, and how evidence demonstrates their effectiveness.
Here's how this translates to a more efficient IRL response process:
- Semantic Search & Discovery: When an IRL arrives, semantic search capabilities move beyond keyword matching. The system understands the intent of the request and identifies relevant artifacts based on their relationships within the Trust Graph. For example, a request for “evidence of access control operation for PII data” won’t just surface documents containing those keywords, but the most relevant (semantically) artifacts and all evidence demonstrating control operation (e.g., access logs, user provisioning reports, pen test results).
- Automated Contextualization: The system automatically presents the evidence within the context of the IRL request. Instead of simply providing a file, it highlights the sections relevant to the request, identifies the control it supports, and links it to the associated risk and policy. This eliminates the need for manual correlation and creates a clear, verifiable audit trail, significantly reducing the follow-up requests.
- Evidence Validation Support: While human review remains crucial, the AI can assist in evidence validation by flagging potential inconsistencies or gaps. For instance, if the evidence is outdated or doesn't fully align with the control objective, the system can highlight these issues for further investigation. See our previous blogpost on Audit Readiness for more information about this.
- Rapid Response Generation: The analyst can quickly review, validate (with AI providing confidence scores on data integrity – flagging potentially outdated or inconsistent information), and compile a complete, contextualized response. The system can automatically generate a response document, pre-populated with the evidence and a clear explanation of how it addresses the IRL item.
- Reduced Burden on Stakeholders: Eliminate the endless cycle of ad-hoc evidence requests, freeing up control owners to focus on their primary responsibilities.
This isn’t about replacing GRC expertise. It's about augmenting it. By automating the mundane tasks of evidence collection and correlation, we free up GRC professionals to focus on higher-value activities: analyzing risk, improving controls, and driving a more proactive GRC culture. The result is not just faster, more comprehensive audit responses with reduced burden to all stakeholders, but a more robust, resilient, and defensible GRC program. The focus shifts from finding the answers to understanding the answers, and demonstrating that understanding effectively to auditors.