Business data is the fuel that powers the modern enterprise. However, a security breach can quickly turn data into a significant liability.
That risk is accentuated as businesses generate and store massive quantities of personally identifiable information (PII). Cyber attackers intentionally go after PII, and neglecting to locate and safeguard it can have severe financial and reputational consequences.
Contractors, subcontractors and Managed Service Providers (MSPs) working within the defense industrial base hold CUI (controlled unclassified information), which needs to be discovered for CMMC 2.0 compliance reasons.
Accentuating the risk, on August 6, 2024, it was reported that a hacker leaked 2.9 billion records with personal details from National Public Data. The leaked PII included full names, addresses, and SSNs — all in plain text.
To secure their data, organizations need to have a holistic map of all their sensitive data: its location, the associated risk, degree of compliance, and level of protection against cyberattacks. Creating this map is known as PII data discovery, a subset of sensitive data discovery.
In the past, manually discovering and categorizing sensitive data was highly labor-intensive. Data files may have been moved years ago, but most data is unstructured, and identifying data types was unfeasible.
Using emerging, advanced data discovery technology, IT and MSP leaders can more quickly and easily locate, classify, and safeguard all their PII data.
PII Data Types Found Using Discovery Tools
PII data is vitally important, as it falls under numerous data protection laws, and its misuse puts companies under severe risk. There are several types of PII that companies must protect:
- Social Security numbers, credit card numbers, passport numbers, driver’s license numbers, and email addresses
- Patient identities, insurance information, and medical records are examples of protected health information (PHI, HIPAA).
- The Payment Card Industry Data Security Standard (PCI DSS) includes credit card and financial transaction data.
Benefits of Using These Tools
Data Visibility: Due to increased unstructured data (as much as 80–90%, according to MIT), detecting sensitive information manually has always been prohibitively challenging. PII discovery solutions provide much-needed insight into where data sits within the network, tracking its origin and movement throughout its lifecycle.
Risk mitigation: An IBM report finds that the average cost of a breach is $4.4M. While large enterprises can potentially weather the storm, many small and mid-sized organizations cannot. According to Globalscape, compliance fines can have an even more profound impact, with hard and soft costs averaging nearly $15 million. By identifying and classifying all sensitive data, organizations can make security decisions based on risk, data type, lifecycle stage, persona, and many other attributes.
Compliance: Data privacy laws such as the HIPAA, CCPA, and GDPR have increased the demand to protect PII. Software for finding, classifying, and protecting PII automates these steps, assisting with compliance. DoD contractor and subcontractors that handle CUI will need to become CMMC 2.0 certified to be awarded contracts.
Scalability: As data volume grows unabated, organizations need solutions that automatically detect sensitive network data from creation to end-of-life. Along with detection and classification, this software can automatically apply security, governance, and compliance protocols to ensure all data is handled consistently across various organizational environments.
Key Features of Sensitive Data Discovery Tools
Data discovery software performs data scanning, identification, and classification. This enables an accurate risk assessment based on the number of data records, types, and levels of exposure.
Automated Scanning: The software connects to an organization’s data sources (databases, warehouses, cloud storage, file systems, and applications) through APIs or built-in connectors. AI algorithms can automatically detect PII data records such as email addresses, credit card numbers, and Social Security numbers by scanning structured and unstructured data across these sources.
Data Classification: After PII has been located, the software can categorize it through analysis to understand the data content and context and apply predefined rules based on the data type and any applicable laws (such as PCI DSS, GDPR, and HIPAA).
Risk Assessment: As part of the analysis, data mapping shows the movement of sensitive data throughout its lifecycle, illuminating the relationships and contingencies that create risk. Depending on the accessibility and sensitivity of the data, a risk score can be produced to highlight where companies have ongoing exposure.
Continuous Monitoring, Reporting, and Alerting: As data security is not a one-time event, these tools continuously monitor data sources for new, modified, or deleted data, helping organizations maintain an up-to-date picture of their data landscape. Additionally, they should log all of this data to simplify compliance reporting.
These tools help decision-makers make well-informed choices and meet the required standards by generating reports on the locations and kinds of personally identifiable information found. They can also deliver notifications about possible dangers to data security.
The Importance of Data Discovery
Apart from the benefits of knowing where all sensitive data is within an organization (which often leads to other efficiencies), the primary purpose of conducting data discovery is to enhance data security.
Every organization has unique data security needs and compliance mandates. Their requirements may vary depending on their vertical, geographical location, business size, and numerous other factors. Today’s organizations typically meet these requirements through a suite of interconnected components, including:
- Zero-trust architecture (multi-factor authentication, continuous monitoring)
- User-based access control
- Endpoint security
- Security and event management (SIEM) solutions
- Intrusion detection and prevention systems
- Encryption technology
- Data Loss Prevention (DLP) software
- Data backup/recovery
Once an organization has a complete picture of its data landscape, it can leverage these tools to fine-tune its data security. However, utilizing multiple security systems to address data security can be complex, inefficient, and non-comprehensive.
A single comprehensive data security solution focusing directly on data identification, classification, and reporting offers a less complex solution to manage while filling the data security gaps inherent in a multi-product initiative.
