Within that data life cycle, organizations are obligated to keep information safe — make sure they’re not compromised in breaches. According to a recent survey, the potential of exposed sensitive data is what worries business leaders the most. With significant data breaches such as T-Mobile, Western Digital, and NextGen in the news, users are acutely aware of the dangers that can occur when they trust businesses with their sensitive information. Retaining visibility, meeting compliance, and having control over data is a necessity for any business today. The key to protecting personal data is proper data management. A few features data governance applications must have to protect organizations against cyber breaches are discussed below.
Keeping Track of Sensitive Information
Cybercriminals are primarily after private user information. They want to use it as a weapon for ransom or sell it online. Having full visibility of sensitive data and keeping a record of who is attempting to access it is an integral step for keeping it far away from bad actors.
Keeping Track of Sensitive InformationAchieving Data Compliance to Prevent Data BreachesUsing Machine Learning to Detect Risks EarlyRating Risk in User-Friendly ReportsCombining Data Management and Threat Hunting
It’s no surprise that the top three industries that are commonly targeted with cyber attacks that expose personal information are:
Healthcare Finance Manufacturing
All of them are vulnerable to breaches during which bad actors attempt to steal and leak personally identifiable information. Why? Because businesses in these industries store a lot of valuable data within their infrastructure. For example, one of the latest data breaches happened to NextGen, a provider of healthcare software designed for keeping medical records. Social security numbers, dates of birth, full names, and home addresses of over 1 million patients were exposed in the incident. Hackers accessed the system using employee credentials stolen online. Data governance applications can be used to identify, categorize and keep track of sensitive user data as well as who has access to it at all times.
Achieving Data Compliance to Prevent Data Breaches
GDPR, CCPA, HIPAA, and SOX are more than abbreviations that concern a company’s legal team. Their application has an important role in cybersecurity — specifically in the prevention of data breaches. Compliance is a matter of IT as well. What if a company doesn’t comply with data privacy laws? It recently came to light that Canadian Manulife Bank had lax practices when it came to the protection of the highly sensitive data of their clients. The insider source revealed a disturbing truth — the bank hasn’t been complying with the necessary data policies for years. As a result, the personal data of their clients has been potentially exposed. Hundreds of employees had access to it at all times. The information that the bank has about its clients includes addresses, IDs, birthdays, telephone numbers, and social security numbers. In the wrong hands, they can result in identity fraud. While this is an extreme case of poor personal data protection, it is a reminder of how important it is to protect users’ data. And how negligence can lead to possible fraud and hacking exploits. Top data governance applications automate compliance processes to ensure that the information stored by the company adheres to necessary standards. Laws are susceptible to change, and new data (that has to be compliant) is acquired every day, making this practice essential.
Using Machine Learning to Detect Risks Early
Businesses store high volumes of online documents. When they lack visibility of such files, the cybersecurity teams may uncover that the sensitive data was compromised months after the bad actor gains initial access to the network. The longer it takes the security teams to identify critical risks, the more expensive the attack’s aftermath will be. In some of the latest breaches, it took days and even months before companies noticed the threat and started notifying their customers who were affected by a data breach. Machine learning can discover malicious hackers within the system by detecting suspicious activity within the context of a company. For example, a person that has accessed data outside of their working hours. They attempted to download, tweak, or erase the files they don’t normally need to do their jobs. Data governance applications that utilize machine learning to detect threats such as illicit access on time. That way, even if the criminal bypasses other cyber solutions, their activity will trigger the right alarms early.
Rating Risk in User-Friendly Reports
Not all vulnerabilities pose the same risk. Assessing whether the risk is likely to compromise sensitive data of the company saves time for security teams. Also, it helps them to focus on patching up flaws that do present a critical risk. Security teams are already bogged down with alerts, understaffed, and overworked. Patching up flaws that can lead to security incidents is a long and ongoing process. Accurate reports are essential for teams that need to respond to high-risk threats as soon as possible. On average, it takes between 180–290 days for security professionals to fix the vulnerabilities within the system. Weaknesses that are ranked as critical are the priority, and they might get fixed within 146 days. For low-risk vulnerabilities, it can be around 10 months before they’re taken care of. Best data governance applications make a difference between high-risk cyber issues that are likely to compromise sensitive data and those that can’t wait for the next patching schedule.
Combining Data Management and Threat Hunting
Data protection and management go hand in hand with cybersecurity protocols and solutions. To protect the company against data breaches, it’s necessary to apply a holistic approach to anti-hacking protection. Besides guarding the infrastructure from technical hacking and phishing, it’s necessary to make sure that the data is well–guarded within the system. The protection of private data has a central role here. Data governance applications should continually identify and classify private data as well as use machine learning and automation to achieve compliance and detect malicious insiders early.
