Unveiling the Power of Privacy Personas in Data Protection

For the modern organization, data protection has evolved into a complex and multifaceted challenge. With exponential data growth, more organizations are devoting significant resources to safeguarding their information assets and crown jewels. Ensuring compliance with a growing set of data privacy regulations only adds to the complexity.

The stakes have never been higher: significant financial losses, damage to reputation, and loss of customer trust are just a few of the potential repercussions for not protecting data.

Introducing Privacy Personas: A new innovation in data protection

Protecting sensitive and private data is a comprehensive process, involving numerous steps and strategies. Typically, data protection can be broken down into three key functions: identification, classification, and remediation.

The first step, data discovery or identification, often includes identifying the type of Personally Identifiable Information (PII) present in a document or piece of data. But how can you determine the role or identity that data belongs to?

Explaining Privacy Persona

Imagine a bustling city filled with different types of buildings – residential homes, corporate offices, hospitals, schools, and government buildings. Each building has its own unique security needs. A residential home might only need a basic security system with door locks and a burglar alarm. A corporate office might require more sophisticated security measures like access control systems, surveillance cameras, and cybersecurity solutions. Hospitals need to protect sensitive patient data, schools must protect student records, and government buildings have their own set of classified information to protect.

In this analogy, the city is like an organization’s data landscape, and the different types of buildings represent the various types of data or PII entities. The unique security needs of each building mirror the specific protection requirements of different types of data, depending on the role or identity it belongs to — an employee, a customer, a vendor, a partner, a student, or a patient.

Just like a one-size-fits-all security approach won’t cut it for the diverse buildings in a city, traditional data protection methods that don’t consider the ‘persona’ of the data fall short in effectively protecting an organization’s diverse data types.

Using the Privacy Persona

This is where the Privacy Persona comes in. By identifying not only the type of PII but also the role or identity it belongs to, organizations can tailor data protection strategies to the specific needs of each type of data, much like customizing security measures for each building in a city.

Whether it’s employee data, customer information, vendor details, partner records, student profiles, or patient data, identifying the source of the PII was once impossible. With Privacy Personas, Concentric identifies all of the above.

Concentric doesn’t just stop at identifying PII and its associated persona — we take it a step further by clustering similar documents and assigning appropriate labels to those clusters.

What this means is our solution goes beyond identifying the type of PII by determining the persona associated with it. This additional layer of information provides a more comprehensive understanding of the data, enabling more effective protection strategies.Concentric AI’s unique approach with this fine level of granularity sets us apart in the data protection marketplace.

Setting up the Privacy Persona

The beauty of the Privacy Persona lies in its flexibility. You can set privacy personas by policy or out of the box for certain categories. For instance, legal and power of attorney documents are likely to contain employee data. Documents related to mergers and acquisitions could indicate customer or vendor data. By setting these categories and personas, Concentric Semantic Intelligence provides a more nuanced and effective approach to data protection.

The Importance of Categorization

At the heart of Concentric AI’s capabilities is categorization. It serves as the starting point for determining the type of data and setting privacy personas and types. Without categorization, it would be impossible to accurately identify the source of the PII and set the appropriate privacy persona.

Concentric uses sophisticated machine learning technologies to autonomously scan and categorize data — from financial data to PII/PHI/PCI to intellectual property to confidential business information – wherever it is stored.

Our solution scans organizations’ data, detects sensitive or business-critical content, identifies the most appropriate classification category, and automatically tags the data. We use artificial intelligence (AI) to improve discovery and classification accuracy and efficiency, avoiding the need for endless regex rules and inaccurate end-user labeling. In addition, we can monitor and autonomously identify risk to financial and other data from inappropriate permissioning, wrong entitlements, risky sharing, and unauthorized access. We can automatically remediate permissions and sharing issues or leverage other security solutions and cloud APIs to quickly and continuously protect exposed data.

What about Archetypes?

Concentric AI’s archetypes feature provides unmatched granularity and precision in data discovery and protection.

An archetype refers to a specific type of data or file that contains sensitive or confidential information. For instance, a contract in the legal industry, a tax form in finance services, or a workers’ compensation claim in the insurance field can all be considered archetypes.

Our latest update to Concentric AI’s Semantic Intelligence allows security teams to identify archetypes within their organizations. This new contextual understanding helps identify sensitive data, monitoring for risk, and protecting data at a level of granularity and precision that traditional methods simply can’t match.

Concentric AI stands apart from competitors who lack this level of granularity, coverage, and precision in sensitive data discovery. We leverage Large Language Models (LLMs) to understand the layers of information within each archetype and categorize them accordingly.

By identifying and classifying sensitive information within each archetype, Concentric AI provides organizations with a comprehensive overview of their data landscape. This highlights potential areas of risk and ensures that appropriate data protection measures can be put in place. It’s a significantly more accurate way to find sensitive information and identify data at-risk than the legacy methods.

Leading the way in data discovery, risk monitoring and autonomous protection

The concept of privacy persona offers a fresh perspective on data protection. By identifying not just the type of PII but also the persona it belongs to, we can provide a more nuanced and effective approach to data protection.

Concentric scans data, detects sensitive or business-critical content, identifies the most appropriate classification category, and automatically tags the data. We use artificial intelligence (AI) to improve discovery and classification accuracy and efficiency, avoiding the need for endless regex rules and inaccurate end-user labeling. In addition, we monitor and autonomously identify risk to financial and other data from inappropriate permissioning, wrong entitlements, risky sharing, and unauthorized access. Our solution can automatically remediate permissions and sharing issues or leverage other security solutions and cloud APIs to quickly and continuously protect exposed data.

With innovative exclusive features like privacy personas and archetypes, Concentric Semantic Intelligence enhances an organization’s ability to perform precise risk assessments and employ strong data security measures.

To see firsthand — with your own data — how you can quickly and easily deploy Concentric’s solution and take advantage of Privacy Personas, Archetypes and more, book a demo today. You’ll experience the freedom of classifying your data without rules, regex, or end-user involvement.