Data discovery scanning is the process of going through vast quantities of data within your business organization to find otherwise hidden insights to improve your decision-making. This process includes data discovery, identification, classification risk management, and regulatory compliance.
When data is properly sorted and centralized, you can reduce redundancy, and it allows you the opportunity to analyze new correlations and make more informed decisions. Ensuring that all sensitive user data, such as PII, is appropriately stored and handled is a byproduct of proper data discovery methods.
In the world of corporate compliance, having visibility and awareness of data is synonymous with being legally compliant. This article will explore the many processes and systems that make data discovery and actionable steps for your business to remain within the proper legal framework when handling consumer data.
Key Takeaways
Data discovery scanning comes with unique challenges and best practices designed to keep you legally compliant when scanning through consumer data.
How you prepare your data for being parsed by data scanning tools is equally necessary, and having live-time monitoring systems for anomalies is critical in avoiding potential compliance breaches and maintaining corporate compliance.
Data discovery is on the tip of crossing the Rubicon due to lightning-fast computational power and highly advanced machine learning algorithms. Setting the correct ethical standards is required so we don’t lose sight as technology becomes more sophisticated.
The Significance of Data Discovery Scanning
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Once a data breach happens, it can take a considerable time before it becomes recognized when dealing with dark data sectors. One recent study by IBM on data breaches revealed the average cost for your business of such an occurrence is a staggering $4.35 million.
The leading attack vector was sensitive user data being exposed, followed by an incident from a failure to mitigate third-party vendor risk.
Shedding Light on Dark Data:
Shedding light on “dark data” refers to examining information your business stores but is seldom used for business operations or analytics. When such data gets forgotten in the vast storage repositories, it depletes your ability to uncover crucial insights. Data discovery scanning aims to shed light on the dark data sectors to ensure such data is not misplaced or mishandled.
The most damaging threat to a business is the one whose existence is unknown. The more information you hold, the more difficult it is to categorize it properly and put governing systems in place to keep it safe.
Detecting Hidden Risks and Opportunities:
Hidden within the vast datasets, sensitive or confidential information is at risk of exposure: this can lead to financial penalties, regulatory damage, or even data breaches. Data scanning aims to categorize vital or sensitive data using the right software tools.
When you can categorize the information within your system, it’s much easier to stay on top of any legal regulations or data protection laws.
Your business can facilitate suitable protective measures to safeguard the information when you discover and identify sensitive data through data discovery scanning.
Dark data sectors are not only a potential vulnerability that needs to be addressed but frequently hold untapped and unrealized potential.
Through proper data scanning, you can discover trends, correlations, and patterns that can be turned into innovative solutions and updated strategies. Handling such risks on your own can be daunting, and for this reason, you can opt to outsource compliance to help you stay legally compliant.
Key Components of Data Discovery Scanning
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Automated Data Crawlers refer to any tools that can traverse data sources and gather or index information. As their name implies, these are pre-programmed algorithms that look to match with a given input value when parsing through your data.
These systems can also collect metadata, which can be used to create data mapping visualizations of your stored information.
Data crawlers can navigate databases, file systems, and cloud networks to identify specific data types.
The efficiency of these largely depends on having an already immaculate data mapping process.
When your data is neatly organized, an automated system can maintain it relatively error-free.
However, they have some downsides, as if some information contains an improper format or a typo, it can remain hidden from the parsing attempts. Automated data crawlers are a great way to start the data discovery process, as they are relatively quick and efficient but lack attention to detail.
Machine Learning Algorithms are a more sophisticated data discovery tool where patterns and trends are used to train the algorithm for greater efficiency. Machine learning, being a part of artificial intelligence, can imitate how humans learn when dealing with data and change their behavior accordingly.
Before implementing complex machine learning algorithms, explore the compliance framework to recognize key focus areas for your business.
When viewing an extensive data set as a human, you can see the data, but it’s hard to conclude. In contrast, a machine learning assistant can parse through the data and recognize intricate patterns and correlations. In contextual analysis, machine learning algorithms can recognize the context of data beyond just recognizing patterns.
Data Discovery Scanning Techniques
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There are many innovative ways available to businesses worldwide to leverage data discovery processes. When it comes to sound data discovery legally, certain data compliance solutions can help you lessen the risk of an infraction. Let’s examine the key points of this process:
Content Analysis and Classification
Content Analysis and classification refers to a technique for recognizing what data types exist and how to store them for practical use later.
Identifying Data Types is the foremost step to sorting your information in a meaningful way.
This data can be anything ranging from numerical data, metadata, or contextual information like emails or employee suggestions.
When you can properly categorize all of the data types your business handles, you lay the groundwork for data compliance and seal off many venues of risk.
Labeling Data Sensitivity refers to categorizing data by its levels of sensitivity and importance. Data that is prioritized at the highest is the one that should have the most systems and safety nets installed to prevent a breach or misuse.
This is also crucial in implementing appropriate access controls and security measures, especially when sending data to third-party vendors. We have a dedicated guide on choosing the proper third-party risk management framework for access control.
Sentiment and Emotion Analysis
These techniques are reserved primarily for textual data to derive the sentiments or emotions conveyed within the text. A machine can find it hard to parse through an email and note if the customer is dissatisfied (unless they scan for foul language), whereas a human can see nuances and note the key takeaway.
Extracting insights from unstructured text goes beyond sentiment; more nuanced emotions or themes can be extracted from unstructured text. While this often requires the most advanced machine learning and AI software, it can still be implemented with the correct configuration.
Dealing with the sensitive communication data of your customers requires a top-down accountability approach for your employees. Read more on the topic of What is an Accountability Framework? (The Complete Guide).
This is mainly used to gauge customer feedback and reveal specific areas of dissatisfaction. The AI can then note which emotions correlate with a particular product and give you a general overview.
Anomaly Detection
Anomalies, errors, or issues are inseparable from handling large data sets or merging them. Even with the reliability of software, no system is 100% foolproof, and even if your program does not return a runtime error, it can ignore semantic issues. Semantic errors in programming refer to code that gets correctly executed but leads to an incorrect output. In practice, this can be swapping a customer’s name with their address or swapping their mobile number area code with their street address.
On a more complex level, going beyond a simple data type mismatch, anomaly detectors are integral to maintaining ever-increasing consumer data.
Recognizing Irregular Data Patterns
A properly configured anomaly detection software can catch larger patterns where the expected output does not match a given data scanning process. Anomalies or outliers can be identified by understanding what’s “normal” for a dataset. These might indicate system faults, data entry errors, or other issues.
Flagging Data Security Threats
In the worst-case scenario for your business, some anomalies might indicate security breaches or malicious activities. This is further reinforced by the fact that data discovery is insufficient in giving you real-time irregularity recognition.
For instance, an unexpected and significant data transfer might indicate a data exfiltration attempt. While this can be seen as a string of numbers and symbols to a human, it might be seen as an irregular access point to a machine. Read more on how your business can prepare data compliance solutions before proceeding with security flagging threats.
The key to having a data security threat system is configuring it to run in real-time and send any suspicious activity for review by a human. In essence, flagging data security threats is integral to applied or practical legal data compliance.
Implementing Data Discovery Scanning
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Data discovery scanning is about understanding your business data needs and recognizing which systems, software, and tools are the best for achieving the data scanning outcomes you desire.
Choosing the Right Scanning Tools
Choosing the right scanning tools is based upon a couple of factors: considering your budget, need for support, complexity of setup, and maintenance requirements, you still have many versatile options to choose from. Open-source vs. Proprietary Solutions are currently the two dominant models for data scanning tools.
Open-source systems have their source code available online for your developer team to see and integrate within your systems. These systems are either free or cost significantly less than the development of a proprietary solution. The downside of open-source systems is that they require an incredibly high degree of information system engineering to integrate successfully into your business.
When investing in proprietary solutions, the biggest downside is the cost. Such systems have the benefit of significantly increased flexibility in functions, settings, and features. A proprietary solution can offer you tools specifically built to solve a specific issue.
When choosing between these two, consider how much resources will be spent tuning and implementing them.
Having something is better than not having any data discovery system, so don’t be afraid to start small and gradually learn and improve.
Scalability and Adaptability are two crucial parameters to consider when picking which solution to adopt.
Your selected system should be scalable to facilitate a projected growth of your data storage and requirements. Integrating with new data sources that are currently not implemented should also be weighed in when making an informed decision on your data discovery system type.
Data Security and Compliance
When it comes to implementing new solutions, you shouldn’t open the door to the introduction of new vulnerabilities. Instead, you should aim to bolster your business data security posture.
Encryption and secure scanning means that data, especially during scanning, should be encrypted to prevent unauthorized access. This also extends to the need for the scanning tool to be secure against potential cyber threats. Encryption refers to taking a set of data types and scrambling them via a complex algorithm.
Then, any data sent will require a unique key to unencrypt at the other end. Encryption is a staple in cybersecurity and is required when transferring PII information over the internet or between your departments.
All businesses handling consumer data worldwide will be subject to the GDPR and Regulatory Alignment.
Data protection regulations like GDPR, CCPA, or HIPAA are all crucial to grasp and comply with when creating your data security systems.
A good data scanning solution should be implemented in such a way that it does cause a breach of user privacy or information.
As data scanning falls under the clause of gathering consumer information and interpreting it, your business must be incredibly transparent as to why this is needed, which data is being gathered, and how to handle the situation if a consumer can request to alter or delete said data.
Challenges in Data Discovery Scanning
Data discovery scanning is a potent tool to drive untapped insights into your business operations. However, it’s not nearly as simple as implementing a solution without data security considerations.
Ensuring Data Accuracy and Reliability
As the saying goes, “garbage in, garbage out.” Scanning tools can only be as good as the data they’re processing. Inaccuracies in source data can lead to flawed insights and decisions.
When you rely on a human team to input the information into a dataset manually, you risk unavoidable errors. The more you can automate the process and have safety checks if the data type is wrongly categorized, the more readable and accurate data you will hold.
Handling Vast Amounts of Unstructured Data
Much of the data generated today is unstructured, like emails, documents, and social media posts (although the latter can also be confusing for humans to interpret). Categorizing, interpreting, and extracting value from this sea of unstructured data requires first to correctly categorize the data and second to have capable software to parse through it.
Addressing Privacy and Ethical Concerns
When we discuss terms such as data and scanning, businesses approach it from a different standpoint than consumers do. When one of your clients hears the term “data scanning and gathering of insights,” it can lead to unpleasant emotions and feelings of compromised privacy.
As scanning uncovers more data, particularly personal or sensitive data, there’s a heightened responsibility to handle this information ethically and by utilizing compliance solutions with privacy laws. Having clear communication with your consumers and third-party vendors regarding these processes is a great starting point in establishing trust.
Best Practices for Data Discovery Scanning
Data discovery, much akin to any other legal compliance framework or undertaking, has some already established best practices. Let’s dive right into what constitutes a sound data discovery scanning strategy:
Continuous Learning and Training
Just implementing a data discovery scanning system is not enough. These complex systems require considerable tweaking and feedback to reach their full potential. Consider if you can hire or create specialized departments that deal with the maintenance of such systems, especially when employing open-source solutions.
Auditing and Monitoring
Having a regular auditing process for every department that handles user data or maintains the software that parses through large data sets is paramount. Auditing should aim not to punish your employees but to identify areas for improvement and highlight venues of future potential data breaches and compliance violations.
Responsible Data Handling and Ethical Scanning
You should always prioritize the ethical treatment and processing of consumer data. Above all, your reputation can be the deciding factor between retaining a client or losing them. Consider which rights each data subject has when it comes to scanning activities and don’t infringe on the privacy or ethical standards without a sufficient reason.
Sometimes, balancing more in favor of consumer rights protection at the expense of a less speedy process is worthwhile; in the end, you secure the rights of your patrons.
As AI algorithms become increasingly advanced and as more data is fed worldwide through systems and international information highways, we have to remember that, in the end, we deal with real humans and not just numbers in a system.
Regulatory compliances arise from the need to protect consumers from exploitation, and the purpose of the step non-compliance fines is to discourage unethical data handling.
The Future of Data Discovery Scanning
We must remember that these technologies in the grand scale of our human development are relatively recent, and we still thread in the wild west of data mapping and consumer ethics. As scanning technology advances further, and AI starts to better interpret abstract data to gauge consumer satisfaction, we can expect a more accurate and deeper insight driven by data.
This increased efficiency will be in a constant tug of war between regulatory commissions and data discovery scanning solutions. Read more on our currently available data protection compliance services.
Closing
In the information age, the ability to seamlessly navigate, interpret, and harness data has become a defining factor for your business to stay competitive and successful. There is no going around the fact that organizations grapple with the intricacies of their data landscapes and the importance of effective data discovery scanning.
However, simply understanding its significance is just the first step. The true challenge lies in its implementation, optimization, and alignment with overarching business goals.
Captain Compliance aids businesses in not only understanding their data but also in transforming this understanding into actionable insights. Contact us today to discuss how your business can harness the full benefits of data discovery scanning while remaining legally compliant.
FAQs
What is data discovery scanning?
Data discovery scanning is the systematic process of exploring data sets from different sources to draw and uncover conclusions that can be leveraged by your business’s decision-making process.
Learn more about software designed to help your business handle data regulations.
What is the data discovery process?
The data discovery process contains several steps, from categorizing and identifying data sources to employing automated crawlers to prove metadata, patterns, or anomalies. Preparing your data in terms of risk assessment and sensitivity is crucial in this process.
Read more on the intricacies of proper data risk management strategies.
What is the difference between data search and data discovery?
Data search is typically a reactive approach where you look for specific data based on predefined queries or parameters. In contrast, data discovery is a proactive process of uncovering, understanding, and deriving insights from data.
Read our in-depth guide on data discovery tools for your business.
What is data discovery in security?
In the context of security, data discovery refers to the process of identifying and classifying sensitive or critical data within an organization’s landscape. This extends to the realm of legal compliance when handling sensitive consumer data.
Learn how to handle GDPR data breaches by having the right notification practices.