Now more than ever, today’s enterprises face the challenge of managing and securing scores of data. The massive increase in data in recent years can be attributed to the rise in digital transformation, artificial intelligence and machine learning, and smart devices, to name a few. While these advancements have made clients’ lives easier, security teams are under pressure to prevent data leaks and maintain data privacy; otherwise, they risk potentially hefty fines by regulators, monetary loss, and loss of clients.
In such a situation, security teams need to rely on effective data classification tools. It’s necessary so that sensitive data can be handled appropriately and securely — exactly what Normalyze does with aplomb.
Normalyze is a data security company that was co-founded by cybersecurity veterans Amer Deeba and Ravi Ithal to resolve big data problems. Their team has created an AI-powered, agentless data security platform that discovers, classifies, and protects user data by only using minimal information.
“What impacted information security the most is the fast move into the cloud and all that transformation that happened at a very fast pace,” Amer Deeba told the Information Security Media Group. “We saw companies want to innovate and create new business processes to serve their customers better and faster, and with all of that came a lot of innovation, a lot of building in the cloud, a lot of data moving into the cloud, and all of that created, of course, a lot of opportunities — but came with it a lot of challenges also from a security perspective.”
What Is Data Classification?
Data classification is the process of sorting data based on its value, sensitivity, and regulatory requirements. There are four fundamental types of data: restricted, confidential, internal, and public. Public data doesn’t require any additional protection measures because it’s freely accessible by anyone. Internal-only means employees and personnel can have access, while confidential would require special permissions, authorization, or clearance. Restricted data might include intelligence protected by government regulations or proprietary information which, if accessed or compromised, could cause harm.
Additionally, personal identifiable information is considered to be highly sensitive and must be protected, otherwise enterprises may risk violation of data protection regulations. Any kind of information that can be used to identify an individual, such as names, addresses, financial details, or social security numbers, qualifies as PII.
As modern enterprises often handle volumes of highly sensitive data, it’s essential to have adequate protection strategies in place — and that’s not possible without proper data classification. Companies wouldn’t be able to adequately protect their data if they don’t know which of their data is considered sensitive, where such data is located, and who has access to it. That’s where Amer Deeba and Normalyze enter the chat.
Normalyze and DSPM
Normalyze recently obtained the first patent for data security posture management and will be the first company to provide its users with the full advantages of DSPM technology. As a result, their users will be able to protect their enterprise’s confidential data without compromising its availability or integrity. Some of the key features of its platform that distinguish it from its competitors are:
— Automated Data Classification: Normalyze Cloud Platform can discover data, including abandoned databases and shadow data stores, and their content. Discovered data is then classified based on its type. This provides its users withwill full visibility of their data so that they can adopt appropriate security measures depending on the data type.
— Continuous Classification: The platform can classify data on an ongoing basis, which means that security teams are alerted whenever new sensitive data is added to databases. This allows their users to scale without worrying about all the new data that is being added.
— Granular Data Access: Sensitive data is presented in a simplified manner without complex policy configurations so that security teams can take swift action and resolve compliance issues.
— Anomaly Detection: The platform continuously analyses user behavior to identify suspicious activity, data breaches, and account takeovers.
— Monetary Impact Assessment: It also displays the monetary value of different data stores, should a breach occur, so that enterprises can prioritize specific data stores and allocate adequate resources.
These features increase the core data security of an organization. Concerns that use Normalyze’s platform benefit from its efficiency. Their data is classified at lightning speeds, whether it’s stored in a multicloud environment or on-premises. Their data security posture is improved and they have adequate measures in place to guard their most valuable assets.
However, when enterprises have access to quality data classification tools they can prioritize security measures, respond to incidents and mitigate data breaches, and maintain regulatory compliance at all times.
Said Amer Deeba: “We were born in the cloud, built within the cloud, and we talked to a lot of customers recently that are facing these problems, and the challenges they face is really trying to understand where their data is going into these cloud environments and how to secure it. Some of the basic questions we hear from customers are: Where’s my sensitive data? Who has access to it? What type of access do they have? How can I manage all that risk that data contains and manage the compliance challenges that come with it?
“It’s a big problem, especially if you’re trying to do it across multiclouds, and every company these days is in a multicloud environment. To do it in a way that you can really get that visibility and control of the data that you had when the data was on prem and you had it under control is really the challenge a lot of companies are facing. And security teams are trying to solve it at scale in a multi-cloud type of an environment.”