Effective data redaction is an essential component of achieving compliance with data protection and business intelligence practices. While document authors, along with online users, may be responsible for ensuring that the correct information is shared, it is a company’s job to comply with local laws and regulations as they dictate how sensitive content is handled. To avoid violating these guidelines, it’s essential to match the level of protection that a document requires with the appropriate tools, such as Data Masking or Document Redaction.
“Document redaction, otherwise known as document sanitizing, is the process of blacking out or removing any sensitive information from a document so it can be used and distributed, but still protect confidential information too” [Record Nations, Document Redaction: What It Is, How It Works, and When to Use It]. Document/data redaction is a critical practice for any organization that needs to protect sensitive information, such as financial data, personal information, intellectual property, or classified information. The process of data redaction can either be performed manually or electronically using software that will black out or remove the text (both methods will be introduced in the coming parts of this article).
By performing proper data redaction and other data safety protocols, an organization not only show its cares about its customers’ personal data and privacy, but it also saves companies a lot of hassle by completely eliminating the chances of malicious data breaches. So what exactly is a data breach, and how much can such a malicious action cost companies?
Data breaches happen when the encrypted data on a computer becomes available to an unauthorized party. This could happen due to unintentional or intentional actions by employees, contractors, or other people with access to company resources. In many cases, these actions are legal if they are performed with good intentions (for example, an employee accidentally sharing documents with someone). In other instances, they may constitute illegal activities that violate privacy laws or other regulations.
There are three main types of data breaches:
- Accidental: This happens when someone accidentally loses control over their information. For example, if you lose your laptop or smartphone, the information on it could be accessed by whoever finds it.
- Unintentional: This happens when an employee unintentionally exposes sensitive information via email, social media, etc.
- Malicious: This happens when a hacker gains access to sensitive information for malicious purposes, like identity theft or financial gain.
One common way that data breaches occur is through the use of malware. This is software that is designed to gain unauthorized access to a computer or network, and it can be delivered in many ways, such as through email attachments, malicious websites, or infected software downloads. Once the malware has been installed on a computer or network, it can be used to steal login credentials, track keystrokes, or take screenshots of the user’s electronic devices.
Phishing is another common tactic used to gain access to sensitive information. This is a type of social engineering attack in which a hacker sends an email or message that appears to be from a legitimate source (such as a bank, government agency, or well-known company) and asks the recipient to provide sensitive information, such as a password or Social Security number.
Both Malware and phishing attacks fall under the malicious type.
That being said, data breaches are becoming more common and are a severe threat to all businesses. According to the Identity Theft Resource Center, there were 1,093 reported breaches in 2016, affecting millions of customers’ records. Resulting in a steep increase of 40% from the original 780 reported breaches in the year 2015.
The number of breaches may seem high, and it is! But there are many more that go unreported and therefore do not appear in any public databases. While the number of reported breaches is increasing each year, so too is the amount of personal information that we share online with our friends, family, and co-workers through social media sites like Facebook and Instagram.
With that said, nowadays, many firms have found themselves the victims of cyber attacks or even data leakages of employees’ information that can be potentially exploited by hackers. In order to keep these things from happening, there are several steps an organization can take, on top of them is data/document redaction.
Documents may need to be redacted for a variety of reasons, including:
- Legal compliance: Some documents may contain sensitive information that is protected by laws and regulations, such as personal identification numbers, credit card numbers, and other types of confidential data. Redacting this information can help organizations comply with laws and regulations that protect the privacy of individuals.
- Data sharing: Organizations may need to share documents with third parties, such as partners, vendors, or government agencies. However, these documents may contain sensitive information that should not be shared. Redacting this information can help organizations protect sensitive information while still allowing them to share documents.
- Research and analysis: Research organizations may need to analyze large datasets of documents, such as social media posts or news articles. However, these datasets may contain sensitive information that should not be shared with researchers. Redacting this information can help organizations protect sensitive information while still allowing researchers to analyze the data.
In general, redacting sensitive information from documents can assist companies in safeguarding individual privacy, adhering to legal requirements, safely sharing documents, and also safeguarding their own interests by preventing data breaches and information leaks.
With that said, the document redaction process itself depends on the type of data you’re trying to keep intact. Redacting text is relatively straightforward, but redacting credit cards or other security-sensitive details requires an approach that goes above and beyond typical document redaction techniques. In some cases, you may want to completely remove the details altogether by converting them into a black box or blob that appears on-screen without any distinguishable characteristics.
As stated earlier, the steps of the data redaction process may differ depending on the type of document being redacted; however, the process is quite similar. For example, documents that contain PII must first be scanned into an image file and then reviewed by a human eye to identify sensitive data that is required to be redacted. Once identified, text can be automatically replaced with black bars, white boxes, or other indicators such as dollar signs or hash marks, depending on the sensitivity level of the data and what is required to protect it under GDPR guidelines.
When redacting a document, it’s important to remember that simply removing or blacking out text is not enough. The document must also be protected from unauthorized access, and any hidden data, embedded or linked files should also be identified, and appropriate action should be taken.
In manual document redaction, the process involves manually identifying and removing sensitive information from a document. This can be a time-consuming and error-prone task, especially for large or complex documents and datasets. It relies on human judgment, interpretation, and attention to detail to locate and redact sensitive information, which can make it less consistent and efficient than other methods.
On the other hand, rule-based document redaction is an automated method that uses pre-defined rules to identify and remove sensitive information from a document. The rules can be based on the type of information, such as personal identifiers, financial information, confidential information, or the location of the information in the document. The main benefit of this method is that it can be relatively easy to implement, as the rules can be defined by an administrator or business analyst and applied to the document automatically.
In terms of accuracy, rule-based redaction can be accurate if the rules are well-defined and can identify all the sensitive information in the document. It can be faster than manual redaction if the amount of data is large. However, rule-based document redaction may have limitations when dealing with unstructured or complex data or when new types of sensitive information appear, or sensitive information appears in a new location or format, as it relies on pre-defined rules. Thus utilizing such a method as rule-based redaction is more prone to redaction errors.
To avoid these issues, companies are starting to consider software options that allow them to redact documents with minimal effort and high accuracy. With greater importance placed on the accuracy of the process than the amount of work put into it, as result of the process does not tolerate mistakes. Having said that, a dash of artificial intelligence is added to the document redaction process. This comes as no shock due to AI revolutionizing and helping in the development of new innovations.
In contrast, AI-based data redaction methods can be more effective when dealing with these cases, as they can adapt to identify new patterns of sensitive information. With that said, what is an example of such an AI-infused redaction tool?
Some types of documents that may need to be redacted include:
- Legal documents: Court transcripts, pleadings, and other legal documents may contain sensitive information that needs to be removed before they can be made public.
- Financial documents: Financial statements and other financial documents may contain personal information or trade secrets that need to be protected.
- Medical records: Medical records contain sensitive personal information that needs to be protected to comply with regulations such as the Health Insurance Portability and Accountability Act (HIPAA).
- Government documents: Government documents, such as classified documents and police reports, may contain information that could harm national security if released.
- Business documents: Business documents such as contracts, reports, or memos may contain confidential information that needs to be protected.
- Chatbot: Some conversation history and personal data can also be subject to redaction.
- ML: Training data, validation data, and test data sets for machine learning models can be subject to redaction.
- Customer data: Personal and sensitive information of customers data can be redacted, such as names, addresses, phone numbers, and other personal information.
Overall, there are many different types of documents that may need to be redacted, and the specific information that needs to be removed will depend on the context and purpose of the document.
In conclusion, document redaction is the process of removing sensitive information from documents before they are shared with third parties. The methods available for document redaction include manual, rule-based, and AI-infused. Manual redaction is time-consuming but adaptable, rule-based is easy to implement but limited by pre-defined rules not capable of handling complex data, and AI-infused is powerful and adaptable for more complex data but requires additional resources. The best method depends on the use case and data characteristics.
With that said, document redaction is becoming a norm in organizations as a means of protecting sensitive information from data breaches and compliance with data privacy regulations.