Do you want this done fast, or do you want it done right? Though perhaps true in the recent, more manual past, this false dichotomy is no longer a viable limitation. The entry of AI-driven technological tools, when overlayed with oversight and process, can transform privacy operations. The privacy teams of today can have their cake and eat it too – scale without sacrificing control… and speed, without sacrificing accuracy.
A few of the AI tools that break the speed-accuracy barrier for privacy programs are AI copilots, automated workflows, and agents. While these types of AI tools that bear some investigation individually, note that there is a significant overlap among these broad categories of AI tools, and similar technologies allow for all of them.
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AI copilots
One broad category of AI tools is that of AI copilots. As described by SAP, “an AI copilot is a virtual assistant that can use data and computation to help you get things done more efficiently: from generating content in seconds to gaining data insights with a single prompt.”
There are several different technologies that make AI copilots possible – Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI (GenAI). AI copilots can stand alone, like ChatGPT, or they can integrate into other applications, like e-commerce sites or work productivity tools.
Regardless, these GenAI apps can assist with:
- content generation,
- analytics,
- optimization,
- code creation,
- graphic development and design, translation, and even conversation.
One of the useful features of AI copilots is that the user typically simply “talks” to the tool using normal human language, and the tool can provide results back in a comparable way.
AI copilot applications in privacy
In the privacy space, AI copilots have a multitude of applications. Some of the more common ones include:
- Translating internal policy documents and external privacy notices
- Drafting policies and procedures
- Responding to customer and stakeholder inquiries
- Creating training materials and visual assets
- Writing code for system integrations
- Analyzing privacy metrics and generating reports
- Designing customer-facing privacy experiences
Considerations for AI copilot privacy applications
While AI copilot applications have the potential to give privacy teams enormous lift in efficiency, there are risks. Such as:
- IP and security concerns
- Potential bias in outputs
- Imperfect or inaccurate results
Privacy teams should implement human and automated review processes, vet data sources for legality and quality, and collaborate with InfoSec to mitigate risks.
Automated AI workflows
All organizations must support business processes. AI-driven workflow automation allows organizations to streamline those tasks and activities, making them more efficient and accurate, reducing the amount of human involvement required in the processes, analyzing and reporting on important features, increase scale of processes without increasing costs, and ultimately improving the bottom line and the customer/employee experience.
AI workflow applications in privacy
Any privacy pro will immediately think about the individual rights management process as a benefactor of AI-driven automated workflows. For each individual rights request type – access, deletion, correction, etc. – AI can help set up and manage the end-to-end process, from the online or phone intake mechanism through communications with the data subject, and from backend actions within systems to fulfil requests, to analytics to identify trends and errors.
Other repetitive processes related to privacy that may benefit from AI automation:
- Data inventories/Records of Processing (ROPAs)
- Third party management
- Consent and preference management
- Regular metrics and reporting activities
Considerations for AI-driven automated workflows
Many of privacy’s repetitive, at-scale processes, like individual rights and consent management, are specialized. Vendor systems that include AI capabilities are often available for these types of workflows and can be cost-effective solutions ‘out of the box.’ Similar considerations related to accuracy/bias/security will apply to these tools.
Additionally, any time an end-to-end process is fully automated, there may be other compliance requirements aimed at addressing automated decision-making, so a privacy team implementing an automated workflow will want to thoughtfully insert human intervention where it makes sense and address any automated decision-making rules.
Agentic AI
At the highest level, agentic AI tools can complete tasks without human intervention. For example, agentic AI makes hyper-personalization possible on websites. In this use case, agentic AI tracks user behavior through an e-commerce site, matches with any other known information about that individual, and makes product recommendations or even experiential changes “on the fly” for that individual. Then, agentic AI stores results and learns from those results, improving accuracy over time.
Other agentic AI use cases include treatment planning in the healthcare setting, emergency response to map locations of individuals who may need evacuation or assistance, financial and tax planning, and shipping/driving route planning.
Agentic AI applications in privacy
In the privacy space, agentic AI has the potential to reduce human error and security risk while handling personal information. Privacy teams can use agentic AI to handle tasks directly, but an especially powerful capability of agentic AI is to help monitor and control activities. Agentic AI can ‘watch’ for outliers for compliance-related activities and act, such as blocking access or alerting the privacy team.
For mature privacy programs with robust monitoring and Privacy by Design activities, agentic AI can serve as an early-stage watchdog, freeing up human members of the team from routine control activities until it sounds an alert.
Considerations for Agentic AI
Depending on the numbers of factors that influence agentic AI’s decisions and actions, these tools may require quite a bit of data for training and deployment purposes. For example, if an organization uses agentic AI to automatically re-route delivery trucks, the tool may need to access external data sources as input, such as road construction and other delays, weather, closures, natural disasters, holiday schedules, and vendor employee strikes.
The volume, accuracy, and legality of the data the tool needs are all factors that the company must carefully consider and control. Similarly, a privacy team using agentic AI will want to pay special attention to not only the usual AI privacy issues (consent, legal basis, data minimization) but also to which sources are appropriate from an accuracy and reliability perspective.
Summary
Privacy teams, just as any operational team, can benefit from the efficiency, accuracy, and reliability that AI can bring to its activities. The challenge – one that many teams have successfully met – will be to identify and mitigate the privacy, security, legal, and operational risks. With care, privacy operations powered by AI will level up and eliminate that false “do you want fast or good” dichotomy.
Why Consent and Preference Management matters
As AI tools become more embedded in privacy operations, managing consent and preferences becomes increasingly complex. A fragmented approach can lead to compliance gaps, inconsistent user experiences, and operational inefficiencies.
A centralized consent and preference management partner is essential.
It ensures:
- A single source of truth for all consent records
- Seamless integration across AI tools and workflows
- Real-time updates and auditability
- Consistent application of user choices across systems
In short, excellent consent and preference management is the foundation for governing fast and well, without compromising trust or compliance.
