Home » Data Analytics » Cyber Risk Analytics

Cyber Risk Analytics


Cyber Crime is the most common term in today’s digital era and needs no introduction but analyzing where the gap lies and how can it be bridged is the need of the eleventh hour, According to the Cisco studies Distributed Denial-of-Service (DDoS) attacks are expected to rise to 15.4 million by 2023 which is simply double the attacks that were noticed in 2018. The figure itself indicates the need for better prediction and analysis to reduce or remove the impact of Cyber Risks. Therefore, Cyber Risk Analytics can be seen as the perfect tool to let cyber risk stand weak and digital networks grow stronger. 

What is Cyber Risk Analytics?

It is a multifaceted product that provides actionable threat intelligence to the organization for prioritizing security and support.  The risk rating metric of the Cyber Risk Analytics provides deeper cyber exposure and understanding of the digital ecosystem and prevents organizations from a data breach. This Advanced Data Analytics enables businesses to act proactively in preventing critical information assets using the prediction drawn through Machine Learning and Deep Learning tools. Consequently, Cyber Risk Analytics can be considered as the standard term for cyber risk assessment, rating, monitoring, and control. 

Features of Cyber Risk Analytics

Cyber Risk Analytics is an advanced tool that comprises several features which make it acts smarter against cybercrimes. Some of the popular features are as follows.

RESTful API and ExportsThe analytics access raw data through API or Export to facilitate integration into the existing tools and workflows like ITIL, CMDB, SIEM products, and GRC.
Prioritizing Security GoalsSecurity is the priority objective of every application and network and is based on extensive, updated breach database, and accurate data this analytics provide effective security controls.
Continuous MonitoringUsing these analytics the vendor management becomes seamless too and help perform monitoring task with due diligence. Moreover, generating and reviewing alerts on the issue is the primary goal of Cyber Risk Analytics here.
Data IntelligenceThe tool provides detailed data on the numbers of the data and further helps to use the data in predicting the forthcoming cyber risk by analyzing the trends. As a result, the Artificial Intelligence used in Analytics makes it worthwhile.

Key Metrics Used for Cyber Risk Analytics

There are several fundamental metrics or KPIs to support the execution of Cyber Risk Analytics in long run. Some of the most popularly used Cyber Risk Metrics are the ones with a direct impact on the daily operations of the business or lives of the individual. Hence, here it is represented by bifurcating into two types- Qualitative and Quantitative Metrics.

 Qualitative Metrics

Security RatingsThe security posture score allows provides grades to the company for communicating metrics and analyzing the network security, patching cadence, endpoint security, IP reputation, hacker chatter, social engineering, and leaked credentials. Hence, helps to understand the security of the company.
Intrusion attempt and Success RateThoroughly scanning the antivirus software scan on applications such as web browser, messaging software, email clients, etc. It helps to clearly define the attempts that intruders made and the success rate based on which security protocols are framed.
Non-human trafficIt is extremely essential to figure out and closely observe if the traffic on the website is human traffic or a potential bot attack indicating the risk to the server.
Unidentified devices on NetworkMany times there exists some unpredictable and unidentified devices on the network which are potent to cause severe threats to the device. Hence, the IoT devices along with Cyber Risk Analytics shall be put to monitor.
Frequency of Cyber incidents reportedThe increasing quantum of cybercrime attempts indicates the loopholes of the network. As a result, directing towards the need to re-establish a secure network interface to avoid major losses.
Cyber Security Awareness TrainingThe effectiveness of cyber security awareness training defines the scope of the attacks and their impacts on the network. Usually, well-trained personnel can analyze and predict risks using risk analytics.

Quantitative Metrics

Some of the qualitative metrics for cyber risk analytics are as follows:

  • Mean Time between Failures (MTBF) which helps to determine the network reliability.
  • Through Mean Time to Detect (MTTD) provides analyses of the time duration for which remained unnoticed.
  • Mean Time to Acknowledge (MTTA) refers to the time after receiving an alert and starting working upon on the issue.
  • Mean Time to Recovery (MTTR) indicates the time it takes to respond and resolve the threat and bring the server to recovery.
  • Cost per Incident determines the total spending on investing risks, resolving and restoring them.

Use Cases of Cyber Risk Analytics

The rising cybercrimes did lead to the invasion of Cyber Risk Analytics in the ecosystem but the uses cases of the latter are what makes it stand stronger in the threat–prone environment.

Use CaseDescription
Security Posture AnalysisThe data aggregated from various sources are used to understand the cyber hygiene of the organization. Moreover, the risk ratings calculated using passive and unlimited assessment process helps to provide true insight into its security posture.
Data Breach TrackingThe detailed Metadata extracted using Cyber Risk Analytics is the most comprehensive way to put breach intelligence to use. Being running user – a friendly SaaS portal the research and tracking becomes a quick game here
Systematic Risk AnalysisA thorough understanding of the systematic risk helps to analyze cascading effects of the data application. Furthermore, the aggregation analysis which forms the prime focus of Cyber Risk Analytics is effective in letting organizations effectively deal with potential cyber risk.
Cyber Insurance AnalysisGenerally, raw breach data provides support to actuarial modeling and portfolio management further helping to make decisions based on security ratings. Alongside, it also allows the execution of the Cyber compare module to understand the coverage options for both insured and the vendors.
Vendor Procurement and SelectionCyber Risk Analytics can be the right fit for supporting procurement and selection of the vendor based on ratings to ensure less exposure to threats.  Here, a threat refers to the form of cybercrime arising out of a data breach on the vendor’s part.
Cyber Liability InsuranceThe concept of Cyber Insurance is the go-to –option in today’s sensitive financial and technological world. Hence, the idea of cyber liability insurance which is based on pre-determined statistical and Intelligence models helps to decide coverage options.

Tools and Software for Cyber Risk Analytics

There are multiple tools and software that have made processes relating to Cyber Risk Analytics extremely powerful. Some of the popular Cyber Risk Analytics software are the ones that have the potential to run strong for all the related domains.

Solar Winds Security Event ManagerIt is a log management solution that logs data on a centralized basis by using threat intelligence. As a result, helps to automatically identify and respond to network threats. Such threat intelligence attributes are responsible for analyzing the events throughout the network and comparing malicious threats while highlighting issues with high severity. The alert system of the software allows users to receive notifications regarding security events through SMS or Email.
Data DogThis infrastructure monitoring device is useful for automatically collecting logs from applications in the environment and filters them for security events. Data dog threat detection provides enhanced visibility regarding the malicious threat pattern. Furthermore, helps detect performance anomalies with the use of Machine Learning and assist in planning customized alternatives to handle cyber threats.
Sumo LogicThis log analysis tool comes with Adaptive Signal Clustering and Security Analytics for identifying potential security threats and generating contextual information to resolve the issue.  Its expert visualization practices break down high-volume logs into simpler patterns and allow a clearer understanding of the performance trend.
Log Rhythm NextGen SIEM PlatformIt is a log management framework working on scenario-based analytics and Machine Learning algorithms to trigger alarms for notifying about the problem. Here Machine Analytics is the combination of behavior profiling, statistical analysis, whitelisting, and blacklisting which makes threat identification more profound. Its User XDR  focus on analyzing the behavior of the user and the entity to remediate the risks and Security Orchestration, Automation and Response (SOAR) tool for responding automatically to an incident based on Playbook.
Elastic StackIt is an open-source log management tool is used to collect logs from services like Apache, AWS, Amazon Cloud Watch, Docker, MySQL, Kubernetes, etc. Its anomaly detection feature makes use of Machine Learning as a detection engine and determines platforms’ response to the events. Additionally, the dashboard provides log data in the form of graphs and charts for key performance monitoring.

Final Words

Cyber Risk Analytics is the emerging data analytics type making it the loudest buzz in the technological era owing to its wide range of applications which is the result of growing cybercrimes. Furthermore, indicating towards the need for expert professionals and more profound software with the least loopholes to stop threats from paving in.

Topics in Data Analytics

Hits: 20