Published Jul 19, 2022
Autonomous Information Classification for Effective Data Security and Compliance
Personally Identifiable Information (PII), Payment Card Industry (PCI) data, and Protected/Personal Health Information (PHI) are regulated by authoritative and legal bodies globally. However, in a world of rapidly growing digitization and digital economy, should you also be concerned about vulnerabilities related to other data types?
Also, what about the mapping of critical information, which requires user involvement? How are you going to map a large dataset in a short time?
Let’s find out how autonomous information classification can improve your organization’s data security architecture and policy.
How does Data Classification Help with Enhanced Data Security
Global information security organizations usually classify data into several categories, and the process is known as data classification. PII, PCI, and PHI are among the most critical data for individuals, businesses, and organizations.
Businesses must comply with the regulatory protocols when receiving and storing sensitive customer data, and information security software packages prioritize securing them. Fortunately, it is not difficult to structure and characterize PII, PCI, and PHI.
Information security companies use simple keywords and structure to identify sensitive data. For example, credit card information usually contains the name of the card holder as text, 16 numeric digits, a 3-digit security code, one expiry date, and one issue date in mm/yy format. So, you can simply set a program to identify the pattern.
Once the software identifies the pattern, it moves to the next step to implement a security measure or encryption depending on the protocol.
However, this well-established method has a few shortcomings. Let’s see what they are.
Challenges With Data Classification
Here are the challenges related to the traditional methods of data classification:
1. Unstructured Information or Data Types
Businesses deal with several confidential information every day that is not limited to structured data types only. For instance, your financial reports, employee contracts, and meeting minutes may also contain information you do not want to get leaked. Yet, since those data are not structured in a specific format, a regular security program cannot classify those without human involvement.
2. Data Mapping
Classification is only a part of the data management and security process. Data mapping occurs afterward, where the system matches relevant data fields between multiple databases. The mapping of critical information is complicated and often requires human involvement.
Unlike automated programs or machines, users cannot process a large volume of data within a short time. This has severe security implications because, without classifying the available data instantly, you cannot deploy suitable protective measures to secure the data.
3. Dealing With Insider Threats
Data classification typically deploys a security procedure once a threat is detected from external sources. However, what about internal threats that are related to information already in your database?
A Ponemon study suggests that a total of 6,803 incidents related to insider threats happened in the 278 participating organizations within a 12-month period, which cost the organizations over $15 million. Interestingly, 56% of the incidents happened primarily due to human negligence.
Thus, when you deal with a complicated data set, you need to find a way to deal with human error. Autonomous information classification is your answer.
How Does Autonomous Classification Help with Improved Data Security
An autonomous information classification system scans both your internal database and external information continuously in the background. It also helps with monitoring:
- What information is shared within your organization
- Who is using it
- What is the user’s access levels
- How to secure it
Thus, the system lets you effectively remove obstacles to data assessment, automated protection, and policy creation, while saving your cost and resources at the same time. As a result, you can reduce human involvement, and have an improved and more efficient organizational security system.
If you want to see autonomous classification in practice, Cognni is an excellent choice, as it independently scans your Microsoft 365 solutions to classify the available data types and monitor how it is used. Cognni goes beyond the structured data categories and can even correctly identify new data types, including the non-structured ones.