Big Data Projects – How to Achieve Security and Compliance with Standards

Big Data systems sit on top of streams of sensitive data like financial records, healthcare data, and user activity logs, and much more. Though such data is valuable, it is also greatly risky. For example, a breach, a misconfiguration, or compliance failure can easily wreck credibility and cost millions of dollars in fines.

Protecting literally all ‘Big Data’ is much more than firewalls and antivirus breach tools. Security must be incorporated into each lifecycle of collection, transfer, storage and analytics, and more. At the same time, standards like GDPR of Europe, HIPAA of the USA, or ISO/IE 27001 must be regarded to. Without such compliance, losing access to relevant markets can become a reality or, worst case, even more severe penalties can be imposed.

This article focuses on building a framework of compliance and security for big data projects. More importantly risks, principles, access control, auditing, and more must be included to ensure security in the system.

Risks in Big Data Projects

The process of large data projects come with system speed and greatly scaled information, which only heightens risk:

• Data breaches. The risky exposure of sensitive information like passwords, financial information or health records can hurt reputation and even generate costly fines.
• Insider misuse: Employees and contractors can misuse data, whether by crossing boundaries or going beyond their responsibilities, mistakenly or on purpose. This is the result of excessive permission and absence of access control.
• Infrastructure Vulnerabilities: There can be weaknesses, security gaps or poorly shielded points, in distributed clusters, in cloud settings or on exposed services. Even a single, wrongly configured, port could allow an intruder access.
• Non-compliance: There is no lack of hefty penalties as well as legal and operational restrictions to a business should they choose to defy the rules and laws of GDPR or HIPAA.

These exposures should be elaborated on in order to build proper and reasonable defenses. For practical strategies to mitigate such risks in big data environments, explore resources at https://svitla.com/expertise/big-data/.

Core Principles of Big Data Security

The most important principles of strong big data security, for instance, are the following:

• Privileged access: Users and services are granted the minimum access possible to perform their roles.
• Duty segregation: To minimize the changes of system misuse, control of the system, regular evaluations, and system construction, are dispersed.
• Information defense in depth: Data collection, transmission, storage and analysis are each placed behind individual protective layers.
• Transparent Encryption: Sensitive data, whether in rest or in motion, is encrypted by default.
• Proactive defense: Automated tracking and supervisory systems are in place to capture malicious action.
• Design compliance: Legal and compliance regulations are purposefully and strategically placed within the system.

The principles are the most important and instrumental in building a strong foundation. Advanced analytics will not make up for lack of security discipline. Tools for scaling are not enough.

User Management and Access Control

In Big Data projects, there are different personnel like Administrators, Developers, Engineers and Analysts and all have different access levels. Appropriate access control enhances accountability and stops misuse.

• MFA (Multi Factor Authentication) for example, is an addition to passwords that can use biometrics or tokens.
• RBAC (Role-Based Access Control) is assigning permissions according to particular positions to make certain that access to relevant data is guaranteed.
• ABAC (Attribute Based Access Control) is the contextual addition of access control such as time, place, and type of device.
• Use of the principle of separation of duties ensures that no single person can control all the primary activities, such as key control and auditing.
• Periodic access right reviews and revocation: Access rights are routinely audited and immediately revoked whenever an employee exits a project.

These measures ensure that access is transparent, enforceable and resilient to both outside and insider threats.

Encryption and Key Management

Encryption still remains one of the best line of defense in Big Data perimeter security.

• Encryption in transit: Data to and from servers, apps and users is protected by TLS protocols, VPNs and tunneling
• Encryption At Rest: Disks, databases, file systems and cloud storages must be encrypted with combative algorithms like AEA-256, to prevent the use of backups and used disks from being accessed.
• Managing the keys: The power of an encryption system can never be stronger than its keys. Securing the keys in KMS or HSM systems ensures there is no exposure in application code or configuration files.
• Automatic key rotation mitigates the impact of compromised keys.

Tools for encryption that are more automated reduce the chances of developer error by embedding protection systemically.

Encryption is, on the other hand, useless without privacy rules, strict oversight, and meticulous control of the keys.

Auditing and Monitoring

Invisibility intelligence is as bad as no security at all. That’s why auditing and monitoring systems offer the earliest alarm for abnormal activity or an attack.

• Event logging: Every action that is performed by users and services ought to be documented, for example, the attempts to access authentication and retrieving the data.
• Log collection: The distributed Big Data systems that have been consolidated are designed to collect logs on a single repository, and this minimizes the chances of interference.
• Real-time detection: SIEM and other machine learning tools detect abnormal activities like abnormal leaps in data downloads, log in from a strange place, or a sudden increase in access privileges.
• Regular audits: Reviews that are conducted in a set period can detect configuration that is misallocated, accounts that are dormant, and breaches in the compliance framework before they are taken advantage of.

Monitoring systems, in spite of all the shortcomings, are able to control the impact of a breach by making sure that there is speedy detection and response.

Meeting International Standards

You cannot have security without compliance. From, geography to sector, standards serve as the minimum as a guiding principle to protecting your data everywere:

• GDPR (Europe): Guides the collection, processing, and storage of personal data. It grants users the rights to request erasure and request data portability.
• HIPAA (US): Covers medical records, enforcing stringent storage, transmission, and access controls as well as continuous monitoring.
• ISO/IEC 27001 (All World): Gives a systematic approach on managing information security and continuous risk assessment.
• Other Frameworks: ODI, CCPA, PCI DSS and other sectoral regulations also need to be taken into account.

The gaps which lie within compliance provide a fully enhanced trust with the partners, customers, and regulators while ensuring the organization is safeguarded from legal and financial penalties.

Embedding Security into the System Design

Security cannot be an afterthought, nor can it be a bolt on. Adding it retroactively is expensive and disruptive. So, it must be, it’s best incorporated from the outset:

• Automation: Access control, encryption, and monitoring policies should be automatic to limit human error as much as possible.
• Scalability: When new data sources and new nodes are added, the systems which are protecting the data must maintain the same level of security as the rest.
• Testing: Routine penetration tests, as well as threat modeling, help to diminish the possibility of weakness which attackers can exploit.
• Security by design: Promote the idea of considering security as a building block of the system, instead of a restriction to creativity.

Integrating security from the start helps organizations save on costs, minimizes operational friction, and helps with compliance as systems develop and change.

Conclusion

While Big Data can provide tremendous value, it can also pose serious threats. Data breaches, improper internal usage, insufficient infrastructure, and compliance failures can erode the value of sensitive data and the trust of clients.

Going forward, an organizational system of Least Privilege, enduring controls of access and protection, encryption throughout, ongoing and automatic audits, and compliance with global standards, is the right way to go. Designing security features from the beginning is then a business accelerator; it enables companies to protect sensitive information, remain compliant with regulations, and improve the business’s competitiveness.