data risk analytics

When assessing cybersecurity risks and analyzing the data they have, IT teams are likely to make mistakes. It’s inevitable, and it’s human nature.

Long hours of managing systems that secure companies, night shifts, and non-stop alerts can all be common causes of errors.

How can you help understaffed and overworked cybersecurity teams?

To assist overwhelmed teams, companies use data risk analytics. What is that exactly, and how can it improve the security postures of businesses?

Let’s find out.

What is Data Risk Analytics?

Data risk analytics (DRA) refers to the analysis of data to compare the behavior of humans and computing systems. The results are shown in the report that is generated to show all the high-risk threats that have to be managed by IT teams.

DRA decreases the number of security risks and detects unwanted activity within your systems early.

Nowadays, it’s automatic and utilizes a combination of artificial intelligence and machine learning to conclude whether the system is at risk of potential incidents.

How Does Data Risk Analytics Improve Cybersecurity?

The goal of data analytics is to show whether there are indications of an already compromised part of the system.

Some ways in which it can strengthen security include:

  • Giving a bird’s-eye view of potential risks
  • Allowing the discovery of threats and flaws early
  • Generating reports of the state of security and possible solutions
  • Highlighting high-risk problems

Increased Risk Visibility

In data security, having visibility of potential risks refers to knowing who can access sensitive data. The information also has to be protected and businesses have to know where it can be found in your system at all times.

The overview shows whether the information is likely to be exposed in a data breach. It also sheds light on the flaws within your system that could lead to attacks on the network.

Threats Discovered on Time

Early threat discovery is essential for companies. The longer the threat actor is inside a system, the more access they have to sensitive data.

The cost of a cyberattack increases with every minute. Unpatched flaws or suspicious activity can turn into incidents that have to be removed before they harm the company’s reputation and finances.

Leaked corporate intelligence and getting ransom notes that reveal users’ and clients’ data has been gathered in a breach are some worst-case scenarios. They can be avoided with early interventions.

Comprehensive Analytics in Generated Reports

One way data risk analytics can aid with the early discovery of threats is by using AI and machine learning to compare attack surfaces. 

As the tool juxtaposes the current state of security with the previous, it can conclude if there are signs of unauthorized use of credentials or of other practices that violate the set norm.

Findings after the analysis are summarized in the generated report. Comprehensive documentation of the latest status of security is easy to understand, and it shows risks that might be difficult to trace manually.

Emphasis on High Risks in Reports

One feature that makes automated data analytics cost and time-efficient is that it separates the high-risk problems in the system. 

In regular circumstances, IT teams are flooded with constant alerts. Many of these notifications indicate low-risk threats that will not likely result in major incidents right away. 

Aware of this, some important issues may fall under the cracks for the members of IT teams that discard them as false positives.

The final summary highlights risks that have to be mitigated right away from low-risk threats. What’s more, it pinpoints the part of the infrastructure that is most critical and offers possible solutions to patch up the flaws.

Benefits of Automated Data Risk Analytics

Data risk analytics is a helpful tool for your IT teams who manage security because it can help them overcome the common pitfalls of manual analytics and risk assessment. 

Common challenges that teams encounter in risk analytics include:

  • Lack of uniform terminology for key issues
  • No universal approach to how to mitigate threats and flaws before they turn into incidents

What makes the lack of uniform definitions for threats challenging are the varied circumstances within which the issues appear. There’s also a versatile understanding of security depending on the members of your team.

Every business has different needs, attack surfaces, and cybersecurity needs. Therefore, the context within which the risks appear will vary from one organization to another. What may be a high-risk flaw for one company might not lead to a major data breach for another.

What’s more, every business has IT teams and cybersecurity experts with versatile backgrounds and points of view on the issue. For example, they may not even agree on a single definition of the term “risk”.

Ideas on how to patch up flaws in the system and improve security can differ as well. That can cause disruption in the dialogue between members of your team who have to react promptly to fix the issue in the system.

Automated risk analytics includes the suggestions that your teams can implement and strengthen the security. They don’t have to follow them to the letter, but this option can facilitate the decision-making processes when it matters the most.

How Does Data Risk Analytics Improve Security?

To sum up, data risk analytics uses AI and machine learning techniques to discover threats within the system on time.

The process is automated and can be customized based on the versatile needs of the company and the contexts within which the data circulate.

What’s more, DRA it’s a helpful tool for IT teams that are overwhelmed with the increasing workloads and incessant alerts of every minor threat within the system.

Data risk analytics highlights high-risk threats that have to be prioritized and decrease the chances of the exposed systems and undiscovered breaches for months on end.