An Overview Of Identify Data That Requires Confidentiality In Cybersecurity
By Tom Seest
When it comes to cybersecurity, three fundamental security concepts – also known as the CIA triad – are at the core. Ensuring your data remains safe is crucially important.
Confidentiality protects sensitive data against unauthorized disclosure by limiting access. Integrity ensures data corruption and tampering by making sure that it’s free from errors and inconsistencies.
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Table Of Contents
Cybersecurity defines sensitive data as any data that should be protected against unwarranted access or disclosure, including personal information like social security numbers, banking details, and login credentials. Any exposure of such sensitive data can cause irreparable damage both to users as well as organizations owning them – something which could prove financially devastating for both.
There are various types of sensitive data, but the two most frequent are personal and company information protected by law and other policies that apply. Other sensitive types include business intelligence, military secrets, and intellectual property that also fall into this category.
Identification of sensitive data is a challenging endeavor that requires understanding the risks and impacts associated with data leaks as well as conducting an in-depth assessment of users, devices, networks, applications, information sources, and users in order to classify all such sensitive information appropriately and store it securely.
Initial steps must include defining the sensitivity level of data. This could involve looking to federal regulations or industry-specific rules; healthcare organizations, for example, must comply with HIPAA, which protects patient and medical information.
Once the level of sensitivity has been determined, data can then be classified by its level of risk. If sensitive financial information needs to be classified as high-risk – this will protect both the organization and employees against potential monetary losses as well as legal ramifications in case of breach.
Data can also be evaluated based on its effect on an organization, assets, or individuals. For instance, companies selling products may not want their sales figures made public as this would provide competitors with access to research information they could use against them.
Because sensitive information could damage a company’s reputation and operations if exposed, its customers and suppliers could become dissatisfied with their experience and services provided by that business.
However, identifying sensitive data can often be challenging due to the personal preferences and application contexts of users. Existing works tend to classify all sensitive data under one category, making identification inaccurate for most users and potentially assigning false positives as sensitive.
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At Cybersecurity, identifying sensitive data is of utmost importance. Personal information, such as credit card or social security numbers, represents a potential treasure trove that malicious actors could exploit for financial gain or strategic advantage – it also makes an easy target for malware attacks, phishing scams, or password-based assaults.
Sensitive data poses one of the greatest security threats to organizations’ privacy and security, yet identifying it can be challenging. Sensitivity often depends on user preferences or application contexts – making it challenging to develop a standard list of data that are likely sensitive and then apply it across applications without endangering user privacy.
Thankfully, several solutions have been proposed to accurately identify relevant data (Supor; Huang et al. 2015; UIPicker; Support Vector Machines; AutoCog; Whyper; Qu et al. 2014).
Most approaches use keyword searching of descriptive text to detect sensitive data, but their ability to handle unseen or hidden texts remains limited. Unfortunately, none of these techniques has ever been able to correctly identify semantic meaning of descriptive texts based on keyword analysis alone.
Success lies in identifying sensitive data from its users’ perspectives by enabling them to define categories that most closely mirror their preferences in a scalable fashion – our approach S3 does just this.
By employing a learning-based technique, S3 identifies relevant data by extracting semantic concepts from user-provided texts and then employs intelligent algorithms to distinguish important from less important information. As a result, it provides a novel yet scalable approach capable of withstanding repeated iterations with millions of data points as test input.
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Cybercriminals commonly target sensitive personal information (PII) for identity theft, fraudulent financial transactions, and other crimes. Criminals use malicious software, phishing emails, or socially engineered website links to breach computer systems and gain access to people’s data.
Sensitive personal data includes anything from payment details and customer payment information to birthdates, health records, and proprietary corporate information. Your business might have specific needs when it comes to protecting such information; retailers need to protect customers’ credit card and bank account numbers from fraud, while hospitals must safeguard digitally stored medical records and patient profiles from hackers.
Identification of sensitive places is the cornerstone of assessing whether a system or device requires extra protection from security and privacy threats. One effective method for doing so is taking an inventory of devices that store information containing sensitive data – including computers, laptops, flash drives, digital copiers, and mobile devices.
Furthermore, it is crucial to assess both the type of data being stored as well as its source. Businesses often receive sensitive data via websites, contractors, and call centers – any additional details you share could make it easier for cybercriminals to gain entry to your system.
S3 is a system developed for unseen texts which detects sensitive data by understanding their surrounding descriptive text. To classify words/phrases as sensitive categories, first, transform to vector representation before searching concept space for K nearest vectors under a predetermined threshold of similarity distance.
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