Data is now recognized as one of the most critical assets an organization owns. As Peter Drucker presciently said, "What gets measured, gets managed." However, before you can measure and extract value from data, it must be properly classified and protected.
As a data analyst and information security geek, I have seen many organizations struggle with ad-hoc data classification approaches. The volume and variety of data make it hard to gain visibility into what exists and how to safeguard it. In this extensive guide, I share my perspectives on implementing a methodical data classification program based on experience and research.
Why Data Classification is Critical
Let‘s first understand why it‘s so important to classify data. According to the Ponemon Institute‘s Cost of a Data Breach report, the average cost of a data breach is close to $4M. Breaches result from exploiting weaknesses in how data is handled. By identifying sensitive assets early on, impact can be minimized.
I advise organizations to shift their mindset from reactive security to proactive risk management. Data classification enables just that by allowing you to focus protection on what matters most. Over my years advising clients on data risk management, I have found these to be the top drivers for classification:
Pinpoint Your Crown Jewels
Do you know where your most sensitive and critical data resides? Many organizations do not have complete visibility. Classification reveals those crown jewels that require the highest security so the right people, processes and tools can be applied. For instance, user credentials, product designs, trading algorithms, payment data all tend to be of the highest sensitivity.
Comply with Regulations
From GDPR to HIPAA, there are growing compliance mandates around data security and privacy. Drafting policies and demonstrating compliance requires knowledge of the type of data held. For example, GDPR requires personal data to be documented, encrypted and subject to strict access policies.
Reduce Overspend on Security
One global bank found that nearly 40% of its cybersecurity budget was being spent on protecting routine marketing data because everything was treated as sensitive. Data classification provides the foundation to allocate security investments to where it matters most.
Improve Data Quality
When I advise clients on analytics, poor data quality turns out to be the killer issue. In one case, inaccurate customer data cost a retailer $15 million in misdirected marketing annually. Classification enforces processes for managing sensitive data which improves overall veracity, consistency and quality.
Enable Sharing While Securing
Collaboration on data can lead to game-changing business breakthroughs. However, lack of clarity on ownership and sensitivity makes people reluctant to share internally or externally. Classification breaks down those barriers through standard processes for responsible data sharing.
The incentives are clear. Research by Aberdeen in 2021 found that organizations that leverage classification enjoy 68% greater accuracy in discovering sensitive data, 49% reduction in time taken to meet compliance audits, and 45% lower information security risks.
Now that I have hopefully convinced you on why classification matters, let‘s look at how to classify data. Based on my consulting experience, here are the most common approaches:
A simple and intuitive way is to rate data based on damage potential if compromised or exposed. Typical levels are public, internal, confidential and highly confidential. Sensitivity can be determined through questions such as:
- Will it cause financial loss or compromise operations?
- Will it violate laws and regulations?
- Will it impact customer trust or cause reputational damage?
Another method is to classify based on the type of content within the data. For example, personal data, financial information, intellectual property, legal documents, etc. Each content type can have a predefined sensitivity level associated with it.
By User Role
Here data access is controlled based on the user and their role. For example, employee records may be classified as internal and available to HR staff only. Similarly, trade secrets may be marked as confidential and limited to heads of product teams.
By Compliance Objective
You can also classify data based on compliance obligations such as HIPAA, PCI DSS or GDPR. This attaches scope, responsibilities and controls as mandated by the regulation to the specific data.
Often a combination of approaches works best. For example, patient records can be designated as protected health information (content type) and classified as confidential as mandated by HIPAA (compliance) with access limited only to care providers (user role).
Key Steps in Data Classification
I advocate a systematic approach for building an enterprise data classification program. Based on ISO 15489 records management principles, the key steps include:
Discover and Inventory
The first step is to scan the environment to create a central inventory of data. This requires enumerating all data repositories such as file shares, databases, cloud storage, email etc. Automated discovery tools such Data Tracker and Ground Labs Enterprise Recon can expedite inventory across complex environments.
Analyze and Map
Next, perform analysis on the collected inventory to understand sensitivity, ownership and usage. Both structured and unstructured data needs review. If feasible, data owners should validate the assessment. Output is a data map with core metadata captured.
This key step establishes the reference model to use for classification based on your business objectives, compliance needs and risk appetite. Example taxonomy factors are shown below for a sample healthcare provider:
Classify and Label
Now the taxonomy can be applied to classify data via either an automated tool or manual process. Owners should validate classified data. Appropriate labels at the file, record or database level need to be applied for persistence of classification context.
The classification program needs well-defined leadership and oversight. A cross-functional team headed by the CISO with data owners, legal and compliance ensures governance and issue resolution. Periodic reviews must be instituted.
This step instruments security controls aligned to classification levels through policies. For example, public data requires just baseline controls whereas highly confidential data requires encryption, strict access policies and higher assurance access controls.
Monitor and Enforce
Once in place, access to classified data needs continuous monitoring. Any unexplained anomalies based on the defined policies should trigger alerts and incident response based on established playbooks.
With the foundations above, data classification can grow into an enterprise-wide capability. According to leading analyst firm Gartner, organizations must incorporate classification into key processes such as HR onboarding, vendor reviews, M&A due diligence, application development etc. to scale effectively.
Pitfalls to Avoid
Over the years advising clients, I have seen certain pitfalls plague data classification initiatives. Being cognizant of these issues can help you stay clear of them:
Scope creep: Starting with an overly broad scope across the enterprise. Go slowly in phases focusing on most critical data first.
Complexity: Creating a complex taxonomy with too many levels can impede adoption and compliance. Keep the model simple.
Inconsistencies: Allowing inconsistent exceptions and interpretations dilutes the program. Standards must apply uniformly.
Tool dependency: Believing tools alone will solve the problem. Tools assist but solid data governance is the key.
Compliance fatigue: Viewing classification as just a compliance checkbox. Needs to be part of business-as-usual.
Stale classifications: Not reviewing classifications periodically. Sensitivities change over time.
Lack of validation: Data owners not validating classification introduced by tools or assessors.
Overclassification: Being overly conservative in classification, limiting access to data needed by the business.
Being cognizant of these pitfalls will help you navigate the challenges successfully. Adopt a ‘start small, expand cautiously‘ approach.
Key Considerations for Implementation Success
Based on my consulting experience, here are few key considerations for data classification success that I always advise clients on:
Executive mandate – Earn management endorsement to lend the program strategic importance and priority for adoption.
Phased roadmap – Start with high-risk data, learn and then expand. Manage scope carefully.
Training – Educate data owners and users continuously to foster shared responsibility.
Automation – Reduce reliance on purely manual workflows through machine learning assisted tools.
Integration – Embed classification into systems development, procurement and other data lifecycle processes.
Ownership – Establish clear data trustees accountable for defining, implementing and enforcing classification.
Review cadence – Mandate regular reviews to confirm classified data continues to conform to specifications.
Getting executive direction, phasing judiciously, enabling staff and automating with the right tools can lead to data classification success.
Benefits of Classification
Based on results seen at client organizations, some of the core benefits of consistent enterprise-wide data classification include:
Up to 55% reduction in breach risks through improved sensitive data discovery and protection.
Accelerated compliance demonstrated through documented data classification aligned with regulations.
Better informed security investments focusing budget on assets that need highest protection.
Enhanced data quality via processes for handling sensitive data with care and accountability.
Over 80% improvement in classifying unstructured data through automation versus manual efforts.
Providing structure for data sharing and collaboration by clarifying ownership and access policies.
The returns are clear. As per leading research firm Omdia, 77% of organizations rate data classification as very impactful in improving overall data security posture.
With growing reliance on data for competitive advantage, the importance of prudent data classification and security will only increase. Organizations need a strategic data risk management vision. Evolving techniques such as graph-based classification using knowledge networks, and machine learning driven semantic classification show promise for the future.
Cloud computing and multi-cloud environments present new data classification complexities that enterprises need to stay ahead of. My advice is to start building in-house expertise in data classification now to prepare for the future.
In summary, data classification delivers multidimensional benefits but needs methodical strategy and patient execution. With the best practices above, you can take those first steps on the data classification journey. Reach out in case any of these perspectives need clarification. Now over to you – excited to hear your thoughts!