From manually writing and orally remembering numerical records and other information to data now being created, stored, and traversing across a wide range of systems, networks, and cloud services, data today has come a long way.
As an increasing number of organizations swim in trillions of litres of data, only a fraction of it is clear, accurate, and structured. Today, over 80% of enterprise data is unstructured. This presents a fundamental challenge: how can organizations leverage the tons of treasure trove of data at their disposal and, most importantly, categorize and classify sensitive data?
Identifying, classifying, and mapping sensitive data is crucial to business operations, effective data governance, honoring data subject rights requests, and complying with evolving regulatory requirements.
Additionally, the sensitive data discovery market was valued at USD 8.10 billion in 2023 and is expected to reach USD 35.58 billion by 2032, demonstrating the growing impact of sensitive data discovery in today’s hyperscale data-driven digital landscape.
What is Sensitive Data Discovery?
Sensitive data discovery is the process of automatically identifying, classifying, and mapping data that is considered sensitive. This includes:
- Personally identifiable information (PII),
- Protected health information (PHI),
- Payment card information (PCI),
- Intellectual property, or
- Trade secrets, etc.
The discovery process typically involves the use of automation tools to scan structured and unstructured data across databases, file systems, cloud storage, platforms, and even shadow IT environments. Modern discovery tools leverage AI, pattern recognition, and natural language processing to locate data regardless of where or how it's stored.
Common Challenges in Sensitive Data Discovery
Identifying sensitive data is only the first step in the process. Classifying its sensitivity level is another aspect that enables organizations to set priorities for their security initiatives.
What’s more concerning is the exponential volume of data sprawl across multiple systems, locations, and formats — from on-premise databases to cloud storage, email archives, and personal devices. This creates a lack of centralized visibility, making it harder to distinguish between structured and unstructured data.
Additionally, sensitive data is constantly being generated in real time and in motion across geographies, traversing through shadow IT and rogue data stores, creating blind spots that make regulatory compliance an organization’s worst nightmare.
Importance of Sensitive Data Discovery
From sensitive data identification to classification, sensitive data discovery is at the core of ensuring sensitive data is obtained, processed, handled, and shared appropriately.
Regulatory Compliance
Global data privacy laws such as the GDPR, CCPA/CPRA, HIPAA, and PCI DSS mandate organizations to protect sensitive data. The core step in protecting sensitive data is sensitive data discovery.
These regulations require organizations to demonstrate awareness of where sensitive data resides, how it is used, where it flows in the data pipeline and whether adequate security measures are implemented to keep it secure.
Minimizing Risk
Sensitive data is always at risk. A recent data security report reveals that 99% of organizations have sensitive data exposed to Artificial Intelligence. If organizations are unsure of their data assets, they can’t protect what they can’t see. Hence, data discovery is crucial to assessing the current data state, its type, and residency.
The discovery process exposes organizations to all sorts of truths, particularly unsecured data buckets, unmonitored or improperly stored data, shadow data, data in the hands of unauthorized individuals, etc.
Avoiding Data Breaches & Noncompliance Penalties
Data breaches are a harsh reality that every organization needs to confront and prepare defences accordingly. It takes organizations an average of 204 days to identify a data breach and 73 days to contain it. Additionally, compromises involving sensitive data remain the most common type of data breach.
Noncompliance with data breach requirements under notable data privacy laws can result in hefty penalties, legal action and reputational damage. For instance, the GDPR imposes fines of up to 20 million euros, or up to 4 % of an organization’s total global turnover of the preceding fiscal year, whichever is higher. Discovering and properly managing sensitive data significantly reduces exposure to data breaches and noncompliance penalties.
Improved Data Governance
Sensitive data discovery goes beyond identifying where sensitive data resides or who has access to it by enabling organizations to better organize their data assets and know exactly how sensitive data is being utilized and setting clear rules for how it’s stored, shared, and eventually deleted.
Governance empowers data to be utilized for its intended purposes and securely disposed of once its initially disclosed purpose is achieved, reducing storage costs and security risks.
Sensitive Data Discovery Techniques
There are numerous ways of tracking sensitive data, and the best approach typically revolves around the sheer volume of data an organization holds and the complex web of places where it resides. Here are some common approaches to sensitive data discovery:
Manual Data Classification
This old-school legacy approach is hands down the most common approach organizations employ, where data owners manually examine multiple files and label them accordingly. Although convenient for small-scale organizations with limited budgets, this process is slow, error-prone, time-consuming, and nearly impossible to keep up with today’s hyperscale data volume and if the organization wishes to scale in the future.
Pattern-Based Scanning
Pattern recognition techniques use preset rules, like keywords, to identify data that is classified as sensitive. For example, the scanner can be customized to locate things like credit card numbers or social security numbers. While this approach yields faster results than manual data classification, it struggles with contextual accuracy or complex data.
Automated Data Discovery (AI/ML-Driven)
Modern tools operate at hyperscale volume, processing data at great speeds. They leverage AI and machine learning to discover sensitive data across various data points, including structured databases to unstructured documents. Apart from scanning sensitive data, they learn patterns to understand the context around sensitive data and get better over time. Additionally, they have a proactive approach to handling sensitive data by working in real time and ensuring compliance with evolving regulations.
Best Practices for Sensitive Data Discovery
A robust, sensitive data discovery tool isn’t just about scanning complex databases but embracing automation to monitor data assets in real time, identify vulnerabilities, reduce manual overload, and stay on top of compliance requirements.
Discover Continuously, Not Periodically
Data environments are dynamic and rapidly evolving. New business processes, integrations, or user behavior might sometimes bring up sensitive data out of the blue. Organizations should keep sensitive data discovery running all the time to avoid unexpected risks.
Centralize Visibility Across All Data Stores
Data is scattered across various data points, from on-premises to cloud storage, hybrid cloud environments, and SaaS platforms. Ensure that sensitive data discovery tools scan through all data touchpoints, from Amazon Web Services (AWS) Simple Storage Service (S3 bucket) to Google Drive, so you have a clear view of data at hand rather than it residing in silos.
Classify with Context, Not Just Patterns
Don't only look for patterns. Leverage machine learning and natural language processing to assess the context of data.
Align with Privacy Regulations
Ensure your data discovery strategy accounts for data privacy laws like GDPR or CCPA/CPRA. By doing so, you can evade data exposure and have mechanisms in place that honor Data Subject Access Requests (DSARs) or other compliance requirements to prove compliance. Additionally, organizations should also conduct a comprehensive data discovery and classify regulated data types such as personal, financial and health data to comply with evolving regulatory requirements.
Assign Ownership and Accountability
Assign data ownership to trained individuals and have the ownership visible across the board to all stakeholders so everyone is aware of each other’s responsibilities and access entitlements, minimizing rogue access and unnecessary data exposure.
Automate Sensitive Data Discovery with Securiti
Most organizations face the challenges of having limited visibility into personal data since it is distributed across a large number of on-premises, hybrid, and multi cloud data assets. In the current regulatory climate, it is essential to have complete visibility into all personal data.
Securiti Data Command Center provides all the core features such as sensitive data discovery, classification, catalog, tagging/labeling, and risk coupled with People Data Graph across on-premises and multicloud assets in structured and unstructured data systems.
Discover granular insights into all aspects of your privacy and security functions while reducing security risks and lowering the overall costs.
Request a demo to learn more.
