What are the best practices for monitoring and auditing data access and usage?

Data policies refer to guidelines and rules governing the collection, storage, use, and sharing of data within an organization. They establish standards for data security, privacy, and compliance. Roles in data management are designated positions or responsibilities within an organization related to handling data. Common roles include Data Analysts, Data Scientists, Data Stewards, and Data Owners, each with specific duties in managing data effectively.

Data policies and roles provide a high-level understanding as to what can and cannot be done with data. However, to make them better understandable for all users, further specification by defining how these policies translate to processes is often desired.

What the author is referring to here(in simple terms) is to build a scalable and easily reusable data governance strategy across your organization.The easiest way to do so is to build on previous successes from organizations like NIST or ISACA

Monitoring and auditing data access and usage is crucial for ensuring the security and compliance of an organization's data. Here are some best practices to consider: Define Clear Data Access Policies: Establish comprehensive data access policies that clearly define who can access what data, under what circumstances, and for what purposes. Ensure these policies align with legal and regulatory requirements. Role-Based Access Control (RBAC): Implement RBAC to assign permissions based on job roles and responsibilities. This limits access to only the necessary data for each individual or group. Data Classification: Classify your data based on sensitivity levels. This makes it easier to prioritize monitoring efforts.

The best approach is to harness the potential use of information technology. Use of open source data platform can enhance access to data collection whilst enabling the users to create inbuilt data quality checks in the digital questionnaires. Also simplified clear data collection tools will ensure less chances of errors during data collection. Determining roles and responsibilities and data use access rights for data contributors and owners is key.More importantly is to augment this efforts with quality training for data owners, and data contributors.

Besides implementing logging, using role-based access is a best practice when it comes to preventing people access to data they should not have access to. Doing so makes it clear (based on the previous defined policies) who should have access to what. However, taking this approach requires additional controls to prevent people keeping "old roles" when they switch roles/positions or leave the organisation.

In my experience in ensuring effective data monitoring and auditing, I used the WHO data quality tool embedded in DHIS. The tool/app helps with data profiling, data validation and data cleaning. It also helps in communication, as it has aids supervisors to communicate directly to data officers who are responsible for data collection and data entry. NOTE:- the choice of tools and techniques for data monitoring and auditing (data quality assessment) depends on the specific needs and goals of the assessment, as well as the resources available.

Choose a data logging tool or system suited to your needs, like ELK Stack, Splunk, or custom solutions. Define what data you want to log and track, specifying data sources, formats, and retention policies. Instrument your applications and systems to generate log entries and track relevant events, ensuring proper security measures. Regularly monitor and analyze logged data for insights, anomalies, and compliance with data policies, making necessary adjustments as needed.

Data logging tools on their own are insufficient.Especially if you don't have anyone to observe or monitor them. This is where AI as well as analytics & dashboards become so much more important in identifying who, when & where are trying to enter your networks.

Sifting through the vast amounts of log data manually to detect anomalies is very tedious and should therefore be avoided for regular monitoring. Predictive algorithms can be trained to spot suspected anomalies for you instead and flag them for human follow-up decreasing the workload of those who do the monitoring significantly.

Gather relevant data from various sources, ensuring data quality and consistency through cleaning and preprocessing. Choose appropriate analytical techniques and tools (e.g., Python with Pandas, SQL, or data visualization libraries) to extract insights and patterns. Analyze the data, generate meaningful visualizations or reports, and interpret the findings to draw actionable conclusions. Share the results with stakeholders through clear and concise reports or presentations, highlighting key insights and recommendations for informed decision-making.

Establish clear audit objectives and criteria, aligning them with organizational goals and data policies. Collect and examine data samples for accuracy, completeness, and compliance with regulatory requirements. Identify discrepancies or issues in the data, documenting findings and their impact on decision-making. Implement corrective actions and continuous monitoring processes to ensure data integrity and compliance over time.

Classify and categorize data based on its sensitivity, and restrict access accordingly, following a least privilege principle. Implement encryption techniques for data at rest and in transit to safeguard against unauthorized access. Establish robust access controls, authentication, and audit trails to monitor and track data usage. Regularly update and patch systems, conduct employee training, and adhere to relevant data protection regulations (e.g., GDPR, HIPAA) to maintain data security and privacy.

As human error (e.g., through unintentional mistakes such as misconfiguration, accidental approval or more malicious actions like phishing or disgruntled employees) is one of the major reasons for data breaches it cannot be understated how important this step is.

One of the best practices for monitoring data is to set up alerts that notify you of any failures, delays, or anomalies in the data pipeline. Alerts can help you identify and resolve issues quickly, before they affect the downstream applications or users. You can use different types of alerts, such as email, SMS, or webhooks, depending on the severity and urgency of the situation.

As a final point it’s key to maintain detailed audit logs for data access, storage, and usage to satisfy compliance requirements and ensure ongoing awareness of its importance. Additionally, Generating regular audit reports and sharing them with relevant stakeholders ensures cross-organisational ownership of the process. This ensures transparency and accountability in the data auditing process. Finally, it’s important to store audit records securely to prevent tampering or unauthorized access.

Ainda, existe a necessidade de criar um plano para responder qualquer acesso n?o autorizado ou viola??o de dados, incluindo a notifica??o as partes interessadas, para assegurar o cumprimento das obriga??es legais.