Big data offers vital insights that help firms to enhance business operations. However, traditional privacy methods do not have the scale and velocity to keep up with large and complex data sets.
Using big data properly, firms can ensure customer retention, devise better marketing strategies, and promote better decision-making. But, the vast data is readily susceptible to security challenges. Right data privacy practices reduce risks and secure sensitive data.
As per a recent report by Customer Market Insights, “Global Big Data Security Market 2023–2032,” global big data security will grow to USD 55.16 billion at a CAGR of 14.90%. The growing stats necessitate a framework for privacy protection that understands the volume, velocity, variety, and value of big data.
Challenges Companies Face in Securing Big Data
Real-time Security Monitoring
Real-time security monitoring is vital for big data analytics and its infrastructure. But, it becomes hard to track due to the vast number of alerts and false positives.
Data and Transaction Logs Protection
Large data and transaction logs are stored in multi-tiered storage with auto-tiering functionality. These systems do not track the data’s location.
It exposes new weaknesses due to unknown physical data locations and untrusted storage devices. These result in firms losing control over the data.
Moreover, data transmission between tiers exposes data about user activities that hackers can use. Therefore, firms must protect data and transaction logs to maintain data confidentiality and integrity.
Big data handles users’ sensitive data. Hence, firms must align with many legal and compliance needs and ensure data confidentiality.
To achieve this, they must set granular access control policies allowing only authorized users to access vital user data.
Data Analytics (DA) and Privacy
Firms must ensure privacy when using big data for analytics, due to risks of user privacy violations. Outsourced DA allows untrusted third-party employees to access users’ data.
As firms use big data analytic tools to improve customer satisfaction, they must also use them to protect user privacy.
Non-Relational Data Stores
Firms use non-relational data stores with big data. But, these data stores are immature and unsecured. They pose issues like poor client and server authentication and weak data file encryption support, causing privacy threats.
Big data uses distributed programming frameworks to process large data. But, they lack security. Thus, firms must secure the computations in these frameworks to ensure the maintenance of data integrity.
Big data gathers data from various input devices- endpoints. However, the collected data might have malicious data sent by an untrusted endpoint.
This rogue data affects the firm’s analytical outputs. Moreover, validating all the inputs collected by big data is a challenge.
Internal Data Thefts
Internal data culture allows employees to access sensitive business data. While it facilitates data democratization, it also increases the risks of intentional and unintentional data leaks.
Firms must set legal policies and secure the network with VPN to prevent internal data thefts. At the same time, they can use Desktop-as-a-Service (DaaS) to eliminate the need to store data on local drives.
Ways Companies Can Address Big Data Security Challenges
Centralized Key Management
Key management allows companies to protect cryptographic keys against misuse and loss. It is better than distributed or application-based administration.
Users can access keys, audit logs, and rules via a central point in the system. Also, firms need a dependable key management system to handle sensitive data.
Encryption for big data helps secure static and transit data across large data. Firms must encrypt user and machine-generated data. This approach supports many big data formats and distributed file systems.
Intrusion Prevention System (IPS)
Big data’s robust framework restricts infiltration attempts. An IPS assesses the network traffic and helps companies defend against attacks. IPS stays behind the firewall and flags intrusions before they cause damage.
User Access Control
User authorization is primary in network security. Lack of access control adversely affects the firms and the sensitive data.
Companies must set a user control policy. Policy-driven access control manages intricate use control levels, securing big data platforms from insider threats.
Best Practices to Secure Big Data
Boost Endpoint Security and Track Reliable Certifications
Securing the endpoints and preventing data leakage is essential for robust big data security and seamless operation flow. Firms can ensure safety via Secure Sockets Layer (SSL) certifications.
These certifications facilitate seamless business operations and offer a secure environment for companies. This elevates the “trust” factor of the customer, boosting sales.
Use Cryptographic Approaches
Firms must use conventional cryptographic data to filter and search encrypted data. Searchable Symmetric Encryption (SSE) can help achieve the above, while Identity, Attribute, or Converged-based encryption helps secure big data.
Monitoring big data storage management is crucial. Firms can use an untrusted data repository (SUNDR) to check any changes done by unsuitable server agents.
Digital Rights Management and policy-based encryption schemes help ensure big data security against online and offline attacks.
Regulating access control creates a balance between who can and who cannot access the data. It also involves protecting high-security elements and tracking admin data. Moreover, using a single sign can also help secure big data.
Employee Training and Vulnerability Tracking
Training and conducting workshops can help employees understand the importance of data privacy. Firms must also track vulnerabilities from big data security methods. It ensures the system is not affected by attacks.
Conduct Regular Audits
Despite adopting adequate approaches to ensure big data, firms still need to protect the system from attacks and malicious activities. Therefore, conducting audits is vital to strengthen security.
Audits must involve all the business members and keep the process and its outcomes confidential. Companies can revise or modify the big data security to align with the audit outcomes.
Big Data Privacy: What to Expect
Big data continues to be a valuable asset for companies. As firms store vast data, they must ensure transparency on what data they collect and how they use and analyze it. They need a scalable, balanced approach that combines government regulation and voluntary measures.
Reliable big data privacy tools help access, review, and anonymize personal and sensitive information. It allows firms to analyze legacy data, discover its uses, and integrate it with the latest data sources.
Big data analytics tools are beneficial for analytics initiatives and obtaining a complete customer view. But, it raises issues about the legacy data’s accuracy and the ability to track entities for consent to use their data.
Firms with a scalable data governance policy can efficiently assess data risks and benefits. Also, they can make better decisions, improve customer CX, and retain customers based on trusted data.