Navigating Audit Requirements: Building Trust and Automating Compliance
It is important that enterprises comply with audits. They help build trust in many of society’s most foundational organizations by creating transparency, accountability, and improving business practices. At the same time, enterprise scale compliance is really hard to get right. Enterprises comply with regulatory requirements and achieve certification by implementing robust audit processes, which involve regularly reviewing and documenting their data handling practices, security controls, and internal procedures to ensure alignment with legal obligations. Larger organizations often face challenges in gathering and reporting on this information due to sprawling technologies, teams and siloed datasets.
This complexity makes it crucial such organizations face the challenge head on with standardization and automation.
What data do I need to gather?
When preparing for audits and compliance assessments, understanding the types of data you need to collect is crucial. Regulatory frameworks often require organizations to maintain various types of detailed records to demonstrate compliance. Below, we outline the key types of data you’ll need to gather, along with the relevant frameworks and regulations that require each type of data to be tracked and reported.
| Data Type | Relevant Frameworks/Regulations |
| Access Logs | GDPR, HIPAA, SOX, MiFID II, FINRA, BaFin, PCI DSS, ISO 27001, SOC 2, NIST Cybersecurity Framework |
| User Consent Management Records | GDPR, CCPA/CPRA, PIPEDA |
| Transaction and Financial Logs | SOX, MiFID II, FINRA, BaFin, PCI DSS, SOC 2 |
| Breach and Incident Logs | GDPR, CCPA/CPRA, PIPEDA, HIPAA, PCI DSS, SOC 2, NIST Cybersecurity Framework |
| Data Retention and Deletion Records | GDPR, CCPA/CPRA, PIPEDA, HIPAA |
Although these frameworks impose additional requirements, this provides a solid overview of the key data gathering needed for compliance.
What we can see is that the data types essentially boil down to logs that capture: what happened; when it happened; and the context to explain why it happened. It sounds simple enough, but gathering this data can still pose real challenges.
What’s so hard about gathering data?
Ever worked for a small company or at a company with a monolithic system? In these environments, gathering audit logs and data can be relatively straightforward. With more operations centralized within a single system and fewer moving parts or a smaller team, making adjustments to collect data is much simpler. This is not the reality for modern enterprises.
- Diverse Operational Practices – In large organizations, different teams often have their own workflows making it difficult to establish a uniform data gathering practice. For example, one team may rely on manual processes while another uses automated systems, creating inconsistencies in data collection.
- Lack of Standardization – Inconsistent formats and protocols across various departments and systems lead to difficulties in merging data from different sources. For instance, one service might log with different fields or a different format making it hard to produce a combined report.
- Inconsistent Access Control and Logging – Organizations often enforce varying levels of access control and logging policies, resulting in fragmented data collection and security risks. One department might have robust access control and good logging, while others implement only the bare minimum and store no logs.
- Varying Latency Requirements – Different systems within an organization may require different processing speeds, creating challenges in syncing and analyzing data in real-time or near-real-time. For instance, in a system processing market operations, generating audit logs might introduce undesirable latency.Â
- Multiple Data Sources – Organizations gather data from an increasing number of diverse applications and datasets, from cloud-based platforms to legacy systems, making it difficult to aggregate and maintain a unified view of critical information. With each new dataset, comes a new set of integration challenges.
- High Data Volume – It can be tough to scale the infrastructure and analytics capabilities needed to manage the immense volume of data produced by users and staff in large enterprises, requiring advanced systems for efficient storage, processing, and analysis.
- Acquired Organization Data – When a company acquires another organization, the data from the new entity often follows different standards, formats, and structures, which can make integrating and analyzing the data a complex and time-consuming task.
- Varied Retention Requirements – Different departments or legal regulations may dictate distinct data retention policies, creating challenges in determining how long to store various types of audit data. For example, financial records might need to be stored for several years, while access log data may only need to be kept for a few months.
What key factors define high-quality audit data?
When building a high-quality audit data process and culture, there are several key characteristics to keep in mind. These qualities ensure the data is reliable, complete, and useful for compliance and decision-making. First, it’s important to be able to clearly show what happened. This includes knowing which actors were involved and what actions they took. It’s not enough to just know if money was moved; understanding where it went and who was responsible is crucial for accurate audits.
In addition to knowing what happened, context is key. Actions don’t occur in isolation, and it’s important to understand the surrounding circumstances. For example, knowing the balance of an account or the roles that an actor had on a given account adds necessary context. While some of this information may be stored in other systems, it should be referenced or included in the audit log to make reviewing actions easier later on.
To be useful, audit data must be standardized. Since audit data is often processed in bulk, having a standard format is essential to avoid making compliance efforts more manual and costly. Moreover, having high confidence in the completeness of the data adds value. Completeness allows for more accurate analysis which helps maintain trust. Finally, storing data in a structured format ensures that it is easily accessible for those who need to query it. The data must be accessible for it to be helpful.
How do Styra Customers use Audit data?
Styra customers collect audit data from Open Policy Agent (OPA) instances running across their systems and process it within DAS, our enterprise-grade control plane. This system ensures all key requirements are met: it standardizes data about actions from every service, includes critical context such as policy versions, and is comprehensive, capturing every action where OPA has made a decision. But how do customers get value from this? We dug through years of customer calls and picked out these top, recurring use cases.
Compliance is a significant concern, especially for our financial customers, who are focused on gathering data to demonstrate adherence to regulatory requirements. This need for compliance is often a primary driver behind their initial adoption of OPA, as it provides the necessary audit data to meet these standards. The value derived from this compliance data is substantial, as it enables organizations to ensure they remain in line with regulations and prepare for audits. Fraud detection is another major use case, with audit data from OPA frequently being integrated into systems like Kafka and Splunk for business data analytics.
Beyond compliance and fraud, customers also leverage data for a variety of other day to day purposes. Standardized data provides valuable insights that help customer support teams investigate incidents, development teams troubleshoot recurring bugs, and product teams analyze user engagement across multiple services. Additionally, historical data is often used to validate policy changes through log replay before they are rolled out. Ultimately, whether auditors are due in a few months or tomorrow, the insights from this data offer significant immediate value too.
Where Next?
Reliable audit data is essential for building trust and ensuring compliance. By starting with a clear understanding of the data you need, you can create a proactive approach that prepares you for audits at any time while delivering valuable operational data for analysis in the meantime too. It’s also crucial to equip your analysts with the right tools for reviewing data when meeting audit demands, and the good news is that Styra DAS integrates seamlessly with many of the tools you already use. Ultimately, having reliable, standardized audit data empowers your business to stay secure, compliant, and trustworthy, and we’re here to help make that process easier for you.
Interested to see this all in action? Get a demo of Styra DAS to see how this all works. Finance customers might be interested to dig into our information here. What to chat to us first, get an invite to our community Slack here.