Many big businesses, regardless of size or type, have leveraged big data in their processes to make strategic data-driven decisions to get a competitive edge and optimize their operations. However, there are various big data security issues that CISOs of these businesses have to have resilient security measures to secure the sensitive data against malicious activities.
The tremendous opportunities businesses can seize to gather data from multiple new sources make data big. A digital-first mindset globally has made data a gold mine that business owners can mint to improve their ROI and efficiency. But this digital-first mindset has exposed the data repositories to significant cyber-threats that can cripple essential business operations. Cybercriminals are on the prowl to compromise big data architectures leveraged to save sensitive information of companies, employees, and customers. Successful data breaches on key business resources can have devastating impacts, such as legal litigations, negative brand image, and cash flow disruption. Various big data and data analytics tools are available in the market that organizations can leverage based on their needs. However, one crucial fact to consider while integrating these tools is that they are not developed without considering security a top priority, resulting in Big Data Security Concerns.
Following are a few Big Data Security and Privacy challenges that cybersecurity decision-makers face:
Unauthorized Metadata Modifications
With the tremendous surge in the volume of Big Data, unauthorized metadata changes make it difficult to track modification in data. Moreover, it is also challenging to determine which modifications are relevant and trustworthy post-changes. It is crucial to eliminate unauthorized access to sensitive data and its disastrous consequences, like wrong data sets and untrackable data sources. Embracing user access control and mandatory automated authorization processes for all users is essential.
Another strategy that organizations can embrace is nulling out data. This approach will replace data with null for every unauthorized user access. If basic sensitive information like document owners name, edit history, or software type falls into the wrong hands will have disastrous consequences.
One of the most effective security measures to secure metadata is sanitization. Metadata sanitization is extracting all the sensitive information from the document. Once the sanitization of all the documents occurs, it will be more secure to distribute to a wider audience. Unauthorized metadata modifications jeopardize data security and make things challenging for the teams to make an offer by replicating the previous file.
Enterprises that do not have an effective sanitization process will allow all the edit history access to even a hypothetical client. This approach will help them to spot all the original budget corrections or the scope of work of the previous customer. Hence SecOps teams should consider implementing automated data sanitization solutions to ensure they can only access intended information.
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Less Secure Network Perimeter
Organizations that want to store big data on their IT infrastructure must undergo stringent security checks. However, some systems do not work as expected and cannot segment potentially destructive data among incoming data. As a result, there is a huge possibility that such dangerous data can enter the system, stay hidden, and cause a tremendous amount of damage. Cybercriminals are evolving tremendously and are becoming more sophisticated. CISOs and SecOps teams should consider reimagining their security measures by adopting zero trust network architecture (ZTNA).
ZTNA solutions have a robust digital identity-based perimeter and framework. This framework does not trust any users, device, system, or application, even if registered on the network. As a result, organizations are secure irrespective of their physical location. Because user access control shifts from the perimeter to every device and user. The zero-trust architecture security approach enables organizations to divide their network perimeter into micro-segments, making it challenging for cybercriminals to attack.
Social Engineering Attacks
Another significant Big Data Security and Privacy challenge is social engineering attacks.
One of the most preferred approaches cybercriminals leverage to access sensitive information is phishing. Malicious actors send a masked message making it look like sent from a trusted, known source. Employees that click on these links unintentionally will still expose the organization’s big data to these malicious actors. SecOps teams can leverage gateways to secure their organization from scam emails that might have spam, malware, or phishing attempts. It is crucial to have gateways with antivirus, anti-spam, and anti-phishing functionality that enable the organization to spot bad emails.
However, criminals today are becoming smarter, and they attach a malicious PDF instead of a link. These gateways cannot determine such suspicious emails and isolate them. Security decision-makers can leverage all the robust features these gateways offer to overcome all the current big data security problems and threats. Sandbox is an approach that allows businesses to create an isolated copy of the real environment where companies can open potential malicious emails without compromising the platform’s security.
Employee Theft
As organizations of all sizes and types have tremendously evolved their data management process, users today need access to information in real-time which exposes sensitive data to a few users. Modern enterprises seek opportunities to democratize big data, increasing the risk of resources intentionally or unintentionally leaking data. Employee theft is a significant big data security threat and privacy concern, even for smaller businesses. Decision-makers should consider enforcing stringent legal policies to improve big data governance. Moreover, strengthening the network security with a virtual private network (VPN) will help to embrace resilience in the network.
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Data Poisoning
Machine Learning tools, such as chatbots, are trained on big data. ML and artificial intelligence (AI) tools help to personalize and improve customer interactions. Data poisoning is a technique that malicious actors can leverage to attack Machine Learning algorithms training data. This technique is an integrity attack because tampering with the ML framework’s training data will greatly impact the model’s capability to offer accurate predictions. The impact of big data poisoning can have disastrous consequences, such as logic corruption to Data Manipulation and Data Injection. One of the most effective approaches to overcome this is embracing outlier detection. This approach separates injected elements in the training pool from the existing data distribution.
Overcoming Big Data Security and Privacy challenges
Many security threats will come inherent with big data collection and analytics tools. Business decision-makers can leverage Security and information event management (SIEM) solutions to aggregate security logs and automatically detect potential big data breaches.
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