The Pursuit of Productivity: DevOps, PrivacyOps and AIOps

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The pursuit of productivity and efficiency has been going on for years, and nowhere is this thirst for productivity and efficiency more apparent than in today’s production plants, where concepts like Just-in-Time inventory management, robotics and assembly lines are constantly developed, tested and implemented. The end goal is to devise processes that require minimal resources for the maximum output.

If we use the same lens to analyze the data privacy and IT industry, there are processes set in place that allow for constant improvements in productivity and efficiency. Generally, there are three approaches that have been in practice by the IT and data privacy sector - DevOps, PrivacyOps and AIOps - but what exactly are these approaches to productivity and how do they differ from one another?


We have all heard the term DevOps whether we work in the IT sector or not. This is the first agile software development framework that increases software development velocity via a collaborative cross-functional structure between various teams.

Before the DevOps application development was introduced, teams were tasked with gathering business requirements for a given software program and writing its code. The next step was handing it over to a separate QA team, which tested the program in an isolated development environment. They checked if the requirements were met and released the code for operations to deploy. The deployment teams were further scattered into groups such as networking and databases. This constant back and forth delayed software development by creating bottlenecks.

In other words, the problem arises when the teams work separately, because:

  • Dev is unaware of roadblocks by quality assurance, and the Ops team that prevent the program from working as anticipated.
  • Quality assurance and the Ops team only have a little context of the business purpose and value of the software.
  • Each group has opposing goals that lead to inefficiency and blaming other teams when something does go wrong.

The DevOps approach addresses these problems by creating a collaborative cross-functional environment where teams share responsibility for maintaining the system that runs the software and preparing the software to run on that system with increased quality feedback.


The rise in global privacy regulations such as the CCPA, GDPR and LGPD has led to the development of PrivacyOps. This approach works on the back of IT, legal and security teams collaborating towards compliance with privacy regulations.

PrivacyOps can be defined as a combination of practices, automation, orchestration, and cross-functional collaboration that improves an organization’s ability to comply with global privacy regulations, efficiently and effectively.

The PrivacyOps model brings together legal, data, IT, and information security teams under one umbrella. These teams then operate within a common framework that allows them to collaborate and communicate for the most essential practices of privacy compliance. PrivacyOps framework is based on the following:

  • System of Engagement: The system of engagement brings collaboration between teams towards the privacy-related information in a safe and secure platform. This is deemed safer and more reliable than sending personal data over messaging systems or emails for review and approvals.
  • System of Insights: Using AI, bots, and intuitive visualization, the teams working on PrivacyOps can extract real-time insights about all aspects of privacy compliance, including personal information data risks, DSR fulfillment status, regulatory compliance posture, vendor risks, user consent, etc., all in one place.
  • System of Automation and Orchestration: Automation and orchestration of complex tasks like DSR fulfillment, personal information data linking, consent lifecycle management, recording audit records, etc. helps reduce risks, reduce costs and avoid penalties.
  • System of Records: This helps organizations keep a record of all privacy-related information such as PI linkage graphs, assessments, data maps, regulatory templates, and vendor documents in one place.

Incorporating PrivacyOps can offer your organization with the following benefits:

  • Organizations get a better understanding of data privacy regulations and compliance requirements across all functions of the organization.
  • It offers a real-time view of all data privacy risks that may exist inside the organization.
  • Efficiently accomplish and maintain compliance with changing global privacy regulations.
  • Ensure the reliability of various aspects of privacy compliance across an organization.
  • Increase the expertise and privacy understanding of teams across the organization
  • Enable effective collaboration across various teams, from privacy, legal, IT, cybersecurity, development, marketing, and support groups.
  • Develop a unique market position with trust-based relationships with both prospective and current clients.


In simple terms, AIOps is the incorporation of AI and machine learning into the DevOps approach. The integration of AI and machine learning can benefit the IT industry in the following ways:

  • Ability to process data rapidly: A machine learning model can be trained to allow processing of all types of data generated by the systems. If a new kind of data is added, a model can be easily adjusted and retrained, not affecting the performance. This will ensure data fidelity and data integrity, resulting in comprehensive analysis and tangible results.
  • In-depth data analysis: AIOps can help analyze data and create actionable insights that can then be used by DevOps engineers to undertake necessary infrastructure adjustments. This can help in avoiding performance bottlenecks and offer specific data-based suggestions for infrastructure optimization and operations improvement.
  • Automation of routine tasks: After identifying event patterns, automated triggers can be set in place. This means that when statistics show certain events that always lead to a particular result that requires specific actions to be performed to rectify the issue, DevOps engineers can create the triggers and automate the responses to such events.

AIOps integration can allow organizations to enjoy the following benefits:

  • Uninterrupted product availability
  • Preemptive problem solving
  • Removal of data silos and root-cause remediation
  • Automation of routine tasks
  • Better collaboration

Key Takeaway
The relentless pursuit of productivity and efficiency is leading organizations to constantly develop processes and approaches that facilitate collaboration, reduce bottlenecks and leverage the new found power of AI to give deep insights in real-time.

The evolution from DevOps to AIOps, and now, to PrivacyOps is a clear indication that this thirst for efficiency and productivity will continue to give rise to these approaches and frameworks.

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