The digital age is in its prime. The claim that information is power has never been truer than today – humans create 2.5 quintillion bytes of data every single day. However, this data is useless unless business systems can prune and shape it into actionable business insights. In this way, market behavior can be predicted with pinpoint accuracy – leading to ever-greater data sophistication. These vast repositories of consumer data need to be protected with next-generation security fabric.
The Rise of Big Data
The big data industry is expected to hit $90 billion by 2025. As a consequence of this ballooning focus on data analytics, companies are anticipating the discovery of hidden truths. The race for new data-gathering intelligence is seeing companies restructure data architecture, merge existing databases, and eradicate old collection techniques.
This constant search for consumer insight has resulted in a surge in the quantity of customer data being collected. Unfortunately, many companies have prioritized data collection quantity over responsible management.
A Deeper Delve Into Data Storage
Today’s big data architecture revolves around colossal data storage facilities. Data warehouses play a vital role, as these large storage areas capture and store information in a structured format. These warehouses facilitate the efficient recall of particular data, allowing for analysis that cuts through the noise. A corresponding business intelligence application then produces analytical reports on the data objects being held in each warehouse.
From the vast pools of warehouses, many companies then extract more specific data into a series of data marts or lakes. These hold subject-specific data that’s of particular interest to certain departments or projects. After extraction from the data warehouse, this specific data is transformed and loaded into data lake buckets.
However, not all data is created equal. The pinnacle of analyzable information is structured data. This is organized, with a particular magnitude that’s determined by pre-set parameters. This has a massive impact on the ease of processing, receiving and storing this data, vastly accelerating each process. Structured data can also refer to highly pre-ordered material, accessed and recovered rapidly via simple search engine algorithms. One commonly-used example of this are HR employee tables that detail employee names, roles, and salary.
However, structured data refers only to roughly 20% of all information. This carefully-curated data may be the corporate ideal, but the vast majority of all data exists as unstructured reams of customer information, tweets, blog posts, and more. Almost everything you do online creates data, leaving a trail in your wake. This patternless data lacks any shape or form, requiring an incredible amount of time and effort to parse into usable format. After all, nobody is transcribing and tagging calls, or adding genuinely meaningful hashtags to every tweet. The requirement of processing and analyzing all this data has traditionally acted as a significant limiting factor to big data. Now, however, a continued focus on automation has seen companies make leaps and bounds in unstructured data management.
The final type of data is semi-structured. Existing at a flexible in-between, this information is a random blend of data that’s both structured and unstructured. In almost all instances, this is unstructured data that also includes attached metadata. After its initial creation, further information surrounding the data’s time, place, device ID and more all contribute to its status as semi-structured.
The Key Capabilities of a Secure Data Platform
For data kept within warehouses, it’s common that multiple groups – more than the centralized data team – will have access to these broad swathes of info. A key component to modern security regulations is the ability for a company to regulate and monitor who’s accessing what. Top-notch data streams require similarly in depth data governance. These help streamline and safeguard pieces of sensitive information that you can’t risk being accessed by the wrong people. Security regulations such as GDPR, and industry standards including SOC and HIPAA, focus on access regulation.
A solid data security fabric breaks down the monolithic task of data warehouse security into eight key components. The first of these is the clear definition of the data itself, with data discovery and classification revealing the location, context, and volume of all forms of data, across on-premises and cloud-based. With the data itself identified, a perimeter can be established with a database web application firewall (WAF) that continuously blocks SQL injection and other traditional data exfiltration attacks. With the boundaries of a data warehouse fully established, a user right management tool then allows for the continuous background monitoring of data access instances. Privileged users are treated with particular care, with a focus on identifying excessive, unused and inappropriate privileges. Finally, database activity monitoring allows for a database-wide monitoring system.
With a secure foundation established, the other half of security measures now allow for the identification and termination of malicious activity. User behavior analytics establish a user-focused approach to risk analysis. By establishing a baseline for normal data access behavior, machine learning models allow for the automated identification of potentially malicious users. Following the discovery of risky behavior, the DevSecOps team is automatically alerted. To actively prevent any data loss, data loss prevention (DLP) measures thoroughly inspect all requests; a protective layer that covers data at rest on servers, in cloud storage, endpoint devices, and in motion.
Finally, a forward-thinking data security fabric uses AI and machine learning to look laterally across security events, prioritizing the security events that truly need your team’s attention.