The digital transformation of today’s data industry is a result of the dramatic increase in data generation and consumption worldwide. The only way for organizations to keep up with the continuously growing and changing data is to prioritize data as an asset.
Today, organizations are beginning to recognize the potential of their business data and are working on fostering a culture that prioritizes data and leverages it to drive their decision-making. But putting data first is not enough. As data increases in volume, the threats surrounding its security rise as well.
In this article, we’ll discuss the role of data security and elaborate on the security best practices for protecting enterprise data ecosystems.
Table of Contents
What Is the Future of Data Security and How to Prepare for It?
What is a Modern Data Ecosystem?
A data ecosystem is a system or collection of data, its environment, and its interactions among various business processes and tools. The data environment can include infrastructures, cloud service providers, programming languages, applications, analytical tools, and any other entity interacting with data in its lifecycle.
Data interactions refer to the actions performed on data by its environment, such as data collection, data sharing, data storage, data analysis, etc. Data evolves with time, forcing the environment to change or find ways to adapt to changing business requirements.
The entities involved in a modern data ecosystem include:
Data: Data is central to its ecosystem, and all processes revolve around it.
Data sources: Organizations collect data from disparate data sources. They can be inside an organization or an external third-party source.
Data lakes: To put data first for all organizational processes, organizations have central data storage entities known as data lakes or repositories, where different types of data are gathered and stored. This is where datasets are cleaned and structured for business use.
Data pipelines: Data pipelines are data movement entities that connect all organizational teams to the data lake, where they can pull data based on their specific use cases.
Stakeholders: Stakeholders are any entity that uses organizational data and its insights. It can be data scientists or analysts taking transformed data for reporting or analytics. Business applications are also stakeholders that utilize company data through APIs.
Modern data ecosystems are unique for every organization due to the specific strategies these organizations use for leveraging and operationalizing data. They are reshaping organizations’ data architecture. They are putting data at the center of an organization to promote a data culture that democratizes data and leverages it to drive business decisions. Organizations in a data ecosystem can benefit from having enhanced operations and optimized business outcomes.
What Is Data Security & Why Is It Important?
Modern technologies like cloud computing, machine learning, artificial intelligence, big data, etc., have enhanced the use of data ecosystems by reducing infrastructure requirements and costs. At the same time, data is more exposed to data breaches and vulnerabilities. Moreover, data in the cloud is exposed to more threats than data on-premise.
The growing advancements in technology are, unfortunately, also increasing advanced malware threats. With data theft and attacks on the rise, damage control should not be our only security strategy. There is not much left to salvage once sensitive data is lost. The need of the hour for all modern organizations is employing threat prediction and prevention strategies to reduce potential data security risks.
Data security protects organizations’ data assets from unauthorized access, theft, attacks, loss, etc. From the physical protection of an organization’s hardware to the logical protection of software entities, data protection encompasses the entire data ecosystem to protect it at every stage. An organization’s data policies and regulations also fall under this umbrella. These policies essentially apply to the usage of sensitive personal data like personally identifiable information (PII) in healthcare, finance, data for IoT appliances, or customer data for e-commerce systems.
An effective data security strategy can save an organization’s sensitive data from threats and breaches. It gives a clear view of an organization’s critical information and its entry points so an attack or breach can be blocked as it occurs. Data security also ensures data integrity and reliability in accordance with company policies throughout the data lifecycle.
What Are the Data Security Concerns in a Data Ecosystem?
In today’s digitally connected world, data exchange, sharing, and collaboration have introduced organizations to many diverse data sources. Data sharing has become a common interoperability practice within and between organizations, allowing them to disseminate the same data for multiple uses. Such operations should be governed by organizational policies for effective data management.
Sharing data exposes an organization to many security and regulatory risks. It involves putting your data in the hands of other organizations. Such a practice always carries the risk of data misuse and theft. Incorporating real-time data changes has increased the challenge of maintaining data integrity while sharing data.
For organizations to gain accurate insights, they need to deal with the latest data. Moreover, the differing data governance policies of different organizations can pose regulatory prohibitions on data sharing.
Even with the availability of data security tools and technologies, organizations are still battling security issues like:
Increased chances of human errors
Unauthorized access to critical data
Risk of data misuse and replication
Risk of data loss
Potential vulnerability to cyberattacks
Issues with data control and governance
Data compliance issues
Short-sighted security strategies that fail long-term and cannot scale as the organization grows.
Some underlying reasons for the growing security challenges include:
Lack of knowledge of the organization’s data models, i.e., where the sensitive data resides and how it flows through the organization.
Access control issues or data access by unauthorized users
Lack of monitoring of potentially risky data transfer
Delayed alerting in case of a breach of data privacy
Lack of policy enforcement and adherence to company standards
Related Reading: What is the Best Way to Move My Data Securely
How to Ensure Data Security in a Data-First Ecosystem?
All technologies and tools become useless if an organization lacks an effective security strategy to protect its data environment. To create a secure data environment, organizations must take meaningful initiatives at every level to ensure data protection throughout their workflow. Following are some of the steps and data security best practices for organizations that wish to optimize their organizational data security.
Human Error Awareness
While putting together tools and strategies for protecting their data platform, organizations often overlook their weakest link; humans. Even the slightest mistake by an employee can lead business data to incur massive damages that take a fortune to recover.
Every organization is protective of its data. They can utilize security platforms and educate employees on compliance policies to ensure data protection. Usually, the employees know what their organization is doing to protect data but are unaware of individual security roles.
Staff training should be an essential part of an organization’s security practices. They should be educated on good security behavior through conferences and seminars on data security. Some security practices that every employee should follow:
Using strong passwords and avoiding sharing passwords on any platform
Knowing how to handle sensitive data
Recognizing malicious attacks to take timely actions
Protecting their personal and office systems from unauthorized access
Following encryption protocols for distributing critical information
An added security layer in the form of a human firewall plays a huge role in an organization’s fight against security breaches.
Breach And Attack Simulation
Security platforms are crucial to an organization’s security practices. To ensure the effectiveness of these platforms, it is essential to test their strength against attacks and their adherence to security controls.
Breach and Attack Simulation (BAS) is a security testing method that recognizes vulnerabilities in an organization’s security environment. BAS assumes the role of an attacker to follow potential attack paths and simulates attacks on the system to test its security.
This strategy is effective because BAS is an automated and continuous testing technique that functions non-stop. It gives a clear visualization of an organization’s security environment. It automatically simulates attacks that target an organization’s most critical data and assets and then provides prioritized solutions to its security weaknesses. In this way, organizations can strengthen their weakest links to sustain a secure data environment.
Security Vendor Consolidation
When looking for security tools, organizations typically take one of the two approaches; vendor consolidation or best-of-breed. The problem with choosing the best-of-breed strategy is that no one tool can do it all. But going for multiple tools ties the organization to many vendors that complicate their security processes and administration.
The solution is simple; security vendor consolidation.
Security vendor consolidation is a strategy that converges multiple security technologies to gain their combined benefits at reduced costs. The idea behind consolidation is to get maximum risk coverage while reducing the number of security vendors. The security gaps left by one technology can be covered by another, resulting in high-performance security operations that enhance an organization’s strength against cyber threats.
Data security solutions cover a range of organizational assets located internally (on-premise infrastructures) and externally (e.g., cloud or externally located servers). In isolation, these assets can ensure adherence to security policies. Security control issues arise when data is exchanged between these assets.
This is where a cybersecurity mesh can play an important role. A cybersecurity mesh is a modern security architecture consolidating an organization’s security structure and uniformly securing all organizational assets. This approach to security can simplify the protection of distributed assets by ensuring consistency in security policies.
Cybersecurity mesh creates a scalable and flexible network of security tools that provides a holistic view of an organization’s security environment. A holistic view plays a vital role in catching security attacks in real-time, allowing organizations to timely block data breaches.
Artificial intelligence has come to the rescue of many industries with its ability to compute large data volumes that are growing daily. Cybersecurity is no different, as it also deals with the security of massive amounts of data that can benefit from AI security solutions.
AI can help enhance security measures against malicious attacks. Cybersecurity tools can leverage machine learning algorithms to create self-improving security solutions that learn from previous data to prevent future attacks. These algorithms also allow the prediction of how and where potential security threats exist.
Automating security measures allows for a faster and more efficient response to threats and attacks than manual procedures. As data grows, AI-driven security measures scale and improve. They can automatically and accurately identify the strengths and weaknesses of a security environment.
The best part of an AI-driven security approach is that it keeps organizations up to date with the latest risks and threats that can potentially come from attackers using the same AI technologies to commit cybercrime.
Related Reading: Top 6 Data Security Tools For 2022
What Is the Future of Data Security and How to Prepare for It?
It is not surprising that the latest advancements in the technology ecosystem are setting the stage for advanced cybersecurity attacks. The cybersecurity industry will have to leverage the same advanced technologies to fight these attacks. While some basic security practices will not change, new security solutions will also arise to protect a growing data environment that will increasingly become more vulnerable.
The best approach to prepare for such a future is to keep your organization up-to-date and educated on the latest security best practices as well as the latest trends of cyberattacks. Future attack predictions using AI should be integral to your security approach if you want to stay one step ahead of potential risks. Lastly, learn from past mistakes, improve your security weak points, and do not forget to fill the penetrable gaps in your cybersecurity architecture.
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