How can privacy keep up with AI? Agents that move at the same speed

Innovation is often chaotic and asymmetric for a reason. Unlike the top-down central approach that offers certainty at the expense of flexibility, modern innovation is the outcome of independent silos that are nonetheless interconnected. This modern paradigm, further spiced by LLMs, offers data at a scale that dwarfs previous pivot points, like social graphs and cloud migration.

While this model offers a range of capabilities for the producers and consumers of innovation, it also increases risks around privacy and data quality. These risks cannot be easily quantified and therefore cannot be mitigated. 

Using standard manual assessments to address these risks is akin to using a camel to try and keep up with a unicorn. As the much-discussed “AI Trade” faces headwinds where the ROI of AI comes under scrutiny, it is likely that AI investments will face the same scrutiny other innovations have. 

In order to prepare for that, it is vital that organizations incorporate efficiency into their AI implementations. As part of sound AI governance, companies need to get a handle on their data, particularly personal data to avoid privacy compliance violations. Otherwise, your data collection will be akin to pouring gasoline on the smoldering embers of business risk.

The good news is that rather than just hoping for the best, companies can improve their privacy assessment and data mapping practices by leveraging Privado AI agents and software scanners. 

Run Agentic Risk Assessments that Scale with Innovation

As an example, companies can set agents to populate assessments using existing templates for PIAs, DPIAs, RoPAs, and vendor assessments. Rather than a human-driven process that is susceptible to delays and error, Privado AI can populate your templates end-to-end by running agents and connecting to your existing data map. 

Using Agentic Assessments, stakeholders can populate assessments in minutes by importing relevant documentation. Agents cross-reference the documents and the existing data map in Privado AI to generate answers with cited evidence. Privado AI builds centralized data maps by scanning software in real-time and filling in gaps from Agentic Assessments. This process can be a continuous loop whereby this initial step provides a gap analysis that you can provide to your stakeholders to validate and then fix gaps. 

Agentic Assessments product screenshot

Rather than waiting for a human assessment to catch these issues before a product ships, these scans can run automatically and continuously. Each scan of your internal code or third-party software can detect changes and flag risks, thereby enabling you to not just fix gaps in your data and models, but also identify when your assessments become outdated. That way, your risk mitigation tooling can keep up with your innovation efforts without creating unnecessary processes.

Privado AI’s capabilities do not end here; neither does their applicability.

A Privacy Governance Platform Built for the Age of AI

In fast-moving companies, product managers and engineers often fall out of sync. Once a feature is shipped by engineers, for example, the product managers then update their design specs to match the engineering specs. The other way around is also possible. Product managers update their specs in anticipation of building a future capability without scheduling the engineering work. This is sometimes sloppy but also possibly risky because there is a human assumption that “we will fix things later” and “later” sometimes does not arrive.

Privado’s tooling enables you to scan contracts and CLM systems to build inventory of each contract’s data elements, obligations, etc. You can continually leverage contract data in assessments populated by agents, and therefore flag privacy risks when data processing outcomes conflict with contracts.

All of this collective investment becomes grist to bring privacy risk awareness across the organization but also to vet third-party tools. From the days of the famous Target breach from more than a decade ago, third parties often provide targeted specialization but their data protection processes lack finesse. Given the scale and speed of AI models, the risks and stakes are much higher.

Do Compliance Like Everyone is Watching

Waiting for a defined compliance regime creates uncertainty, but automating it can create visibility and get you closer to certainty.

To that end, Privado AI’s tooling will empower you to populate entire assessments with AI agents, thereby freeing up your legal and compliance teams to focus on strategic risk reduction. 

You can set agents to follow your PIA, DPIA, RoPA, and vendor assessment processes. Using that as a starting point, you can then scan any related product documentation, tech specs, proposals, Jira tickets, etc. This highly automated and extensible capability can help you extract data elements, third parties, processing activities, and anything else needed for your assessments. 

You can mitigate known risks by auto-tagging sensitive data for GDPR, CCPA, and other regulations. Then, rather than hoping for the best, you can discover all third parties receiving personal data and then update data maps in real-time. Over time, you can greatly reduce, if not eliminate manual assessments.

The upshot: rather than having to initiate data protection before an arbitrary line is crossed i.e. data is shared with a third party, you can maintain complete data maps across processing activities. 

Another upshot: rather than thinking of your engineers as disconnected innovators and your attorneys as blocking risk-mitigators, you can ally them for the age of AI. They can collectively drive new development while also buffeting the risks for the business, 

Privado AI is not just a privacy platform; it is about driving efficiency and governance for your company regardless of headcount or roadmap. The size and diversity of our customers means that our capabilities are not just market-leading but also market-adapting.

Come. Join us.