Information Sensitivity: What It Is, The Risks, and How to Protect It

Organizations face a trifecta of challenges when it comes to protecting data: massive cloud migration, the rise of remote and hybrid work, and data sprawl. As these hallmarks of digital transformation proliferate, protecting sensitive data is a critical responsibility for all businesses — regardless of industry.

Sensitive data is everywhere: on-premises, in the cloud, structured and unstructured repositories. In order to achieve robust data protection, it’s important to understand the various types of sensitive information, the risks involved with leaving it unprotected, and the critical role solutions like Concentric AI can play.

What is sensitive information?

Sensitive information encompasses a wide range of data types, each requiring specific protection measures.

Let’s briefly identify these categories.

Personal Identifiable Information (PII): PII includes data that can be used to uniquely identify, contact, or locate a single person. PII could be a name, address, Social Security number etc.

Personal Information (PI): This broader category includes any data that can be linked to a particular individual, extending beyond PII to include things like IP addresses, location data, and even certain types of behavioral data.

Sensitive Personal Information (SPI): SPI, as defined under regulations like the California Privacy Rights Act, includes data that may not directly identify an individual but can cause harm if disclosed. Examples of SPI include social security numbers, racial or ethnic origins, and religious beliefs.

Nonpublic Personal Information (NPI): NPI is particularly relevant in the financial sector and encompasses data that is not publicly available, such as bank account details, credit histories, and other financial data.

Material Nonpublic Information (MNPI): MNPI relates to confidential corporate information that can impact a company’s stock price and can include undisclosed earnings reports, mergers, acquisitions, and other significant business developments.

Private Information: Regulated by laws like the NY SHIELD Act, private information includes a combination of personal data elements that, when pieced together, can be used to commit identity theft or other types of fraud. For example, a combination of a name with a Social Security number or a driver’s license number.

Protected Health Information (PHI): PHI refers to any information about health status, provision of health care, or payment for health care that can be linked to an individual. PHI is strictly regulated under laws like the Health Insurance Portability and Accountability Act (HIPAA) in the United States.

The wide-ranging costs of unprotected sensitive data

When sensitive data is not adequately protected, the risks can have severe consequences for companies, their clients, and other individuals and organizations.

Let’s explore a few of the most common risks:

Financial losses: When we think of data loss, the bottom line often comes to mind first. When sensitive data is lost, stolen or leaked, the financial losses for individuals and organizations can be significant. Costs also include direct costs from any remediation efforts required, legal fees, and damage to intellectual property.

Identity theft: All this sensitive data may be used to steal an individual’s identity, allowing bad actors to make purchases, access bank accounts, or open credit accounts.

Reputational damage: When sensitive data is lost, the company’s reputation takes a hit — which may bring about lost trust from clients, customers, or third parties.

Legal and regulatory consequences: New data protection regulations are being introduced frequently. As such, organizations may face stiff penalties, regulatory action or even legal ramifications for neglecting to protect sensitive data.

Protecting sensitive data with Concentric AI

Concentric AI’s autonomous data discovery and classification solution is designed to address the complexities of identifying and protecting these diverse data types. The platform offers a nuanced and accurate approach to data classification and protection by leveraging advanced machine learning and natural language processing.

There are three key steps in protecting sensitive data with Concentric AI:

• Identification of sensitive data
• Classifying sensitive data
• Remediating risk from sensitive data

Concentric AI can effortlessly identify business-critical and sensitive data, even if it’s not explicitly labeled or categorized.  As sensitive data evolves and grows, our solution adapts and learns on the go, ensuring that new types of sensitive data are promptly identified and protected.

By leveraging advanced deep learning technologies for language analysis, Concentric AI can understand the context and semantics of the data. Our language models go beyond mere pattern matching or rule-based methods, which are cumbersome to maintain and may not identify all potential threats. Concentric AI also compares each data element against baseline security practices used by similar datasets, which can identify deviations or anomalies that might indicate a security risk.

For protecting business-critical sensitive data, it’s important that the data protection solution understands the dynamic nature of data threats. Concentric AI operates in real-time, identifying potential data threats as they emerge. When a potential threat or unauthorized access to sensitive data is identified, Concentric AI takes proactive measures to remediate the issue — whether that’s changing access permissions, notifying security teams, or other appropriate actions to remediate risk.

Leveraging advanced deep learning technology, Concentric AI offers insights that go beyond traditional rule-based solutions, considering each data element’s unique risk profile.

Want to see for yourself, with your own data, how Concentric AI can help with protecting sensitive data?

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