Improve Data Quality in Security Audits

Garbage in, garbage out.

This phrase has long been the bane of many auditors’ existence as they work to ensure an organization’s data and processes are safeguarded through a security audit. These audits often rely on screenshots from source applications for some tools. Other tools ingest the information from the source applications but only test whether a specific parameter is operational – like password complexity – without providing any evidence from the source application. When tools actually receive data from the source, the quality of the data is never questioned, but the use of that data in an audit might be questioned. The auditors themselves may trust the source data to be used as part of the supporting evidence in their workpapers, but they are often left wondering…will the quality hold up?

They are not the only ones wondering. Research shows that while 76% of C-suite executives "have AI and machine learning initiatives included in their company's road map," three-quarters of these business leaders are not confident in the quality of their data.

In this post, we will explore the importance of data quality and how technology can help improve data quality in an audit.

Exploring the Importance of Data Quality

Security audits are now going through their own digital transformation, with data quality as a key driver for change. Governance, Risk, and Compliance (GRC) understands that to maintain data integrity, the information must be complete, accurate, updated, available, consistent across databases, in a valid format, and have any duplication removed. GRC works toward this goal by using a data-driven audit approach and creating standardized, controlled, and repeatable processes for organizations to follow. Data integrity is essential as organizations with poor data quality face significant risk:

Lost revenue: According to Gartner, the average financial impact of poor data quality on organizations is $12.9 million annually.

Flawed decision-making: Incomplete or inaccurate data translates into incorrect insights, resulting in missing critical business opportunities.

Lack of Compliance: As regulations are continuously evolving, good-quality data can help the organization comply and avoid fines in the millions. 

Reduced productivity: Instead of spending time manually validating and fixing data errors, staff can focus on more strategic tasks.

Reputational damage: Misuse or inaccurate data has been the focus of many news pieces. The larger the company, the worse the public relations fiasco will be.  

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How to Improve Data Quality in Audits with Technology

The quality of an organization’s data can be enhanced using technology. AI, analytics, and automation are new techniques being utilized in security audits to streamline repetitive tasks, offer deeper insights, and improve both efficiency and data quality. These technologies empower organizations to set standards and controls to avoid mistakes, and guide personnel when collecting and dealing with vital information. At the same time, they enable auditors to more effectively leverage their expertise, knowledge, and decision-making capabilities. The impact of technology on auditing is vast.

Here are some ways technology is reshaping the landscape of security audits and improving data quality by ensuring the completeness, uniqueness, timeliness, consistency, accuracy, and validity of data.

Pre-engagement and audit planning

Technology plays an integral role in elevating and refining the process of security audits. Through the use of automation and analytics, data from previous security assessments or interim evaluations can be swiftly extracted to determine potential vulnerabilities based on various benchmarks (validity). Audit automation tools not only allow for real-time or continuous security evaluations by pinpointing vulnerabilities but also identify anomalies or deviations from standard security protocols. Integrating AI with automation, auditors can utilize Natural Language Processing (NLP) to scan and summarize a broad spectrum of both public and proprietary data, offering a clear overview of an entity's risk profile (completeness). Drawing from extensive databases, AI in auditing highlights potential risk areas, ensuring precise analysis tailored to specific technology or domain. 

In addition, using artificial intelligence in auditing can allow mapping of standard transaction flows, aiding auditors in understanding business processes and making informed decisions on risk assessment (accuracy). Technology ensures auditors maintain their objectivity by enabling checks on system architectures, conducting background assessments, and cross-referencing with internal security records. This comprehensive approach empowers auditors to discern areas with a heightened risk of security breaches and gain insights into global system configurations and potential vulnerabilities, such as outdated software.

Fieldwork for security audits

The integration of AI, analytics, and automation in auditing is profoundly impacting security audit practices, ensuring robust data security. Automated "audit bots" can swiftly process large datasets, reducing human oversight and enhancing data confidentiality. Analytics tools deeply scrutinize system logs, pinpointing unauthorized access attempts or anomalies, such as duplicate events detected in logs from cloud applications (uniqueness) and helping to ensure multi-cloud security. Contract reviews, streamlined with Optical Character Recognition (OCR), ensure that data privacy clauses are consistently upheld, while AI can quickly detect non-standard data handling provisions (consistency). AI-powered computer vision aids in monitoring physical access to data centers, capturing and analyzing footage for security breaches. In control testing, system-logged reviews, analyzed by AI, can identify unusual patterns, such as sudden data transfers or access at odd hours. Using AI in auditing, can therefore improve data quality by enhancing data security.

Reporting on security audits

The integration of advanced technology in data security audits has considerably optimized the audit reporting process. Audit automation, enabled by AI, has allowed auditors to expedite the resolution of identified issues (timeliness). This accelerates the reporting cycle, making the audit findings more relevant and timely. In addition, the automation of communications has been facilitated. AI tools can now mine data from audit files, generating critical engagement documents, such as the auditor's report and the management representation letter. While standardized templates are in use, AI enhances their application by tailoring them to individual clients, ensuring the reports are not only consistent but also reflective of the unique characteristics of each client, be it an SEC-registered entity or a small private firm. Furthermore, AI's ability to synthesize and analyze data offers invaluable insights. It can collate and evaluate adjusted and unadjusted misstatements and control deficiencies, pinpointing trends or common issues. Such intelligent analysis aids auditors in identifying specific areas that might need heightened scrutiny, ensuring comprehensive and precise audit reports.

Revolutionizing Security Audits: Utilize The Power of Advanced Technology to Improve Data Quality 

The dynamic nature of digital transformation in audits underscores the importance of data quality. As the phrase "garbage in, garbage out" aptly illustrates, the foundation of any security audit is the quality of its data. Traditional security audits, once constrained by superficial screenshots and a passive acceptance of source data, are being revolutionized by technologies like automation, AI, and analytics. These advancements not only streamline and elevate the audit process, from planning to reporting, but also bolster the integrity and accuracy of the data itself. With potential risks ranging from financial loss to reputational damage, organizations can ill afford to neglect data quality. As we move forward in an increasingly digital age, harnessing the potential of these technological advancements to improve data quality becomes paramount, ensuring security audits are both robust and reflective of the rapidly evolving digital landscape.

Discover how using a data-powered Compliance OS can enable your organization to take advantage of technology to improve the quality of your data in security audits.