Good governance of automated processed data can be helpful to secure the data. More control on data security through administrative control, non erasable, permanent data, will bolster the data reliability.

Automated process within pharma manufacturing is really very important from efficiency and compliance perspective. As we know, pharma is having very stringent norms about documentation on timely basis, "Something which is not written, is not done". Based on this, automated processes helps operation team to ease the compliance requirements, however at the same time, data security is critical when it comes releasing of batch. If data is not being captured for any reason in automated systems, the batches can't be shipped to market. Here is the solution, quality by design - ensure the design of automated system ensure data reliability. In case of no data being captured during the batch, there should be interlock to ensure no further process.

I integrate security measures directly into automated workflows rather than treating them as separate considerations. By building in encryption, access controls and monitoring from the start, I maintain both efficiency and protection. My approach focuses on: Embedding security checks within automated processes Real-time monitoring for unusual patterns Regular security audits without disrupting workflows Balancing access needs with data protection The key is making security enhance rather than hinder automation. Success comes from viewing security as an enabler of efficient processes, not a roadblock.

Here are some best practice approaches (there are more but not enough space). 1. Conduct a Risk Assessment: Identify where sensitive data flows through automated processes and assess the risk level at each stage. 2. Implement Role-Based Access Control (RBAC): Limit access to sensitive data in automated processes by setting up role-based access. 3. Data Encryption: Use encryption protocols for data both in transit and at rest, ensuring that data remains secure even when processed automatically. 4. Audit Trails and Monitoring: Set up regular monitoring and logging of automated processes that handle sensitive data. 5. Anonymize or Mask Data: In processes where exact data values are not necessary, use anonymization or data masking.

We live in a data-centric society. Bad data governance and security are a much bigger risk that would merely be exposed by RPA. If an RPA solution is at risk of violating data security, then the design and architecture principles of the development team definitely require refinement. The purpose of RPA would be to replicate the process as is, with the same (or similar) exposure to that of manual execution.