Protecting Your Enterprise with Modern Data Security

Data, data,… and more data. Today, the stream of digital data and documents into and out of business has given rise to three significant priorities:

1. Integrate data into business: to provide value, improve operations, and increase opportunities
2. Manage data: to increase accessibility, interoperability, and relevance
3. Reduce Data Risk: to eliminate and reduce the risks that holding data represents
   
   

While reducing data risk is key in a highly regulated world, arriving at that goal requires knowing your data and being aware of the classes of risk associated with the varying types of information that is stored.

Data Classification – the process of utilizing patterns, analysis, intelligence, and automation to discover and contextualize sensitive information – is the first step in understanding data risks. Risk is ubiquitous and present everywhere in managing today’s data saturated world. Data classification provides us guidance, by associating “risk levels” with classes so we are informed on steps we must take to secure, comply, and protect our business.

In the modern world, cybersecurity, without systematic protection of information and the reduction of data associated risks, is not really “security” at all. As all have seen, there is rarely a day when the most recent data breach doesn’t make headlines verifying to all, that when the bad guys get in, it’s often data they’re after.

An Intro to Data Classification

Data classification is an integral part of preparing data for compliance. It helps build a comprehensive unified view of our internal data world using keywords, patterns, contexts, digital fingerprinting, machine learning, and exact data matching to show us where our sensitive data exists. Modern data classification technologies function across platforms, vendors, and solutions providing quantifiable analysis that prepares us for action in protecting data where it lives and in the variety of ways it’s used.

Data classification empowers organizations with knowledge to manage data risk meaningfully because, simply put, we can’t know what to protect or how to protect it if we don’t know where it is and what’s in it. Classification tells us “what’s in it” and confirms the locations in our digital estate where risk resides (data fabrics, data streams, and documents).

Data classification also brings awareness of another type of risk: the one represented by “outdated data”. Once a business knows what data exists in a document or fabric, it can determine if it’s still needed. Today’s data security technology streamlines that determination making lifecycle review and the disposition at data’s end of life, a process we can automate. 

“Data Classification”, “Data Security”, and “Data Lifecycle Management” together; reduce data risk and improve the integrity of the data we elect to retain.

Advancing Data Protection with EDM Exact Data Matching

EDM is important to classification because it’s a technique that locates specific data values inherent to specific organizations. Instead of finding general data patterns and formats, it finds and protects values on an exact basis, allowing EDM data types to potentially benefit from unique security protocols.

Examples are an employee ID or employee social security number. From a data security perspective, pattern matching is required when we don’t know the exact sequence of characters we’re looking for. But with EDM, we “do know” exactly the sequence of characters we’re looking for (employee’s social security number or employee ID). Knowing the exact string enables specific capabilities to find and protect unique strings because the source of truth is coming directly from our systems.

Exact Data Matching can also continually update these known strings so we always have the most current data sets for EDM controls and protections.

Advancing from Basic Pattern Matching to Artificial Intelligence and Machine Learning

In the technology world, Artificial Intelligence (AI) and Machine Learning are not new concepts. But what remains perpetually new in the data security space, is advances in their use.

Traditionally, classification technologies utilized two fundamental principles to classify data:

• Exact data matching (EDM): Users provide software with attributes like specific numbers or keyword references. The system flags direct matches based on preset parameters.
• Fuzzy data matching: This approach expands from EDM to match patterns, types, forms, fingerprints, and machine learning to match even variants that could include discrepancies due to input errors, the bottom line always being to validate if content is sensitive and therefore requires management.
  
  

AI and machine learning have profoundly accelerated data classification, security, and compliance capabilities. While traditional data-matching methods rely on formulas, expressions, and basic algorithms, advanced AI algorithms and machine learning bring increased nuance to enforcing data security.

Today’s AI systems can “learn” content.

Let’s imagine a title company for example that processes documents into the public record after a real estate transaction closes. AI can be prompted to “learn” this profile of documents by simply feeding stacks of the documents to a data security learning capability. No longer are we simply looking for keywords, we’re looking for content classified in contexts over pages of information. AI can learn, classify, and secure data in ways and at scales not previously attained.

AI also can evaluate complex security “circumstances” relating to how we use data. An example is recognizing when a chain of events represents a risk, and when a single event does not. This is security that’s meaningful and empowers operations by reducing hindrances when contextual use permits valid transmission or sharing of information.

Core Principals of Enforced Data Security

Discovering and classifying data is the first step. The next is how the enterprise elects to govern that data.

From policies to protections, knowing information is sensitive and valuable “must” appropriately result in actions preventing unauthorized access, use, alteration, and transmission when the data is “classed” as sensitive.

In addition to these protections, lifecycle policies must also limit the length of time an organization exposes itself to data risk by preventing the organization from unknowingly or negligently retaining data beyond its useful lifespan.

Fundamental principles of enforcing data security include:

Discovery & Inventory

Know where data is, what’s in it, and what risks the organization is exposed to because of that content.

Classification

Classify data according to the risk identified. Classifications can be considered categories (Restricted, Confidential, Internal Only) to which certain protections and security controls should apply. Data security nomenclature refers to categories as labels or tags. 

Protection & Control

Control data at rest (while stored), in use (by users or systems), and in transit (moving between systems). Protect it both internally and externally according to the categories of risk your organization’s governance has categorized and the controls appropriate to those categories.

Encryption

Lock it down! Not encrypting our data fabrics, documents, points of access, ingress, egress, and transmission is equivalent to casually exposing our super-secret stuff while lounging on a bench in a busy train station. We never know who’s looking or who may grab it. All data which exposes an enterprise to significant legal and regulatory risk should be encrypted both in systems and when possible, within the content itself.

Data Lifecycle

Managing risk includes governing the time in which data is held.

When the risk to retaining data becomes equal to or greater than the value of retaining the data, it is time to evaluate the ratio of return to risk and potentially eliminate the risk. This concept is commonly termed “Data Lifecycle Management”, and today’s data classification technologies serve double duty by using “classification” as a means to manage data lifecycle. In short, know the content, schedule reviews of the content, and incrementally eliminate risk as content reaches the end of its lifespan.

Physical Security

Keeping data safe involves more than just cybersecurity. Providing perimeters of physical security, installing robust access control systems, and training staff to reduce physical exposure all share in the overall data security strategy. Even data backups and archives should be equivalently secured both physically and electronically to prevent breach. None of us want our organization on the front page of the news because we’ve exposed customer data through a forgotten archive.

Improving Compliant Data Flow through Data Security

So how do we get our data security effort started? One word: governance

Today’s organizations must take an informed, disciplined, and structured journey into data security through established governance policy and effective governance implementation. If the bandwidth to do so is challenging, get help or temporarily augment to get started.

Milestones include:

• Establish clear roles and responsibilities in data security.
• Clarify the regulations and internal directives to comply with and build effective governance policy achieving the controls necessary to assure compliance.
• Implement today’s advanced data security tools in a robust manner specifically achieving data governance strategy across all relationships, platforms, and tools.
• Educate and train people to value, align with, and support data governance objectives.
• Conduct regular audits and review governance policies and controls, tuning them to cycle lessons learned and knowledge gained into a growth oriented mindset improving policies and practices.

Risk: Eliminated, Reduced, Governed

If your organization has data, there are cybersecurity risks.

It’s time to get started. A data risk management strategy provides a roadmap to protecting information from compromise, data loss, and breaches. If already in process or if just starting out, outlining protocols is helpful and informative to the process.

Effective data risk management protocols include: 

1. Assessing risk: A thorough evaluation of vulnerabilities assists in reducing the risk of data breaches and other data-related issues. Once these risk factors are identified, including compliance risks, a layered security approach can be adopted, and regular audits can be conducted to support ongoing risk management.
2. Establishing written governance policy: If we fail to plan, we plan to fail. Acquire experience in the statues and regulations governing your enterprise and industry. Gather stakeholders together. Build a written strategic plan (governance policies, standards, matrices) that guide your actions and your team in securing your data estate.
3. Establishing clear roles and responsibilities: Everyone in an organization plays a role in reducing data risk. Ensure they know their role and how they can help protect your organization and your customers from the ramifications of a breach.
4. Integrating access controls: Data access controls allow organizations to restrict data access to only those who need it in the function of their roles. These controls rely on authentication and authorization to verify people are who they claim and have permission to access, use and transmit sensitive and non-sensitive information.
5. Implementing regular data monitoring: Data monitoring is a proactive process in which organizations review their data often to assess its quality, accuracy, consistency, and security. Monitoring, audits, and reviews provide insights into who uses data and how, giving valuable information on your vulnerabilities and improving your potential to secure it. 

Did you know?: 81% of Americans are concerned with how companies use the data they collect. Increasing consumer trust in your organization is essential to a good business and a good reputation.

Enhance Data Security with BDO Digital

BDO Digital can help implement a Zero Trust data security strategy utilizing current capabilities in Data Classification, Information Protection, Data Loss Prevention, Data Lifecycle Management, Communications Compliance, and Insider Risk Management.

We are a leader in Cybersecurity solutions earning Microsoft’s 2023 Security Partner of the Year award. Our enforced data security solutions include leading technologies in Gartner Magic Quadrant, Forrester Wave, and #1 Engenuity Mitre ATT&CK cyber market share. We enhance cyber strategies so organizations can thrive in the face of change and we’re here to serve.

BDO provides a platform approach to data security and privacy. BDO’s thought leadership will serve to align your data security posture management across your digital estate and close gaps in data security visibility so your governance is satisfied, your data is protected, and the specific controls unique to your organization are fulfilled. 

Whatever your business needs, we will take a practical approach to enhance your cybersecurity profile and uncover new areas for optimization and success. 

Book an assessment today or contact us to learn more about the value of our services.