Role of AI in Cyber Security

Real-Time Data Processing on Docker and Kubernetes

Threat and Vulnerability

Security Analytics to understand and detect risk level of vulnerabilities. Artificial Intelligence, Machine Learning and Depp Learning techniques to reduce human error. Identify security breaches and eliminate obstructions.

Big Data Analytics With Apache Spark & Flink

System Security

It involves Network Security, Cloud Security, IoT Security, Malware and Autonomous Security. Operational Management and elimination of security issues, understand risks and threats to internal system.

Big Data Warehousing and Data Lake Solutions

Data and Application Security

It includes analysing advanced threatening behaviour, evolve internal architecture to scan system deficiencies. Data and Application Security involves Security Analytics, Threat Prediction, Spam Detection and Data Privacy.


Analytics Based Services for Cyber Security

  • Digital Risk Management.
  • Identity and Access Management.
  • End Point Detection and Response.
  • Cloud Access Security Brokers.
  • Spam Prevention and Phishing Blocking.
  • Security Operations and Analytics.
  • Statistical Methodology.
  • Malware Detection.
  • Behavioural Analysis.
Real-Time and Stream Analytics Service Providers
Data Analytics Solution for Stream and Real-Time Processing

Cognitive AI Driven Solutions for Cyber Security

Artificial Intelligence Solutions involve right collection of data, representation Learning Application, Machine Learning customisation, Cyber Threat Analysis, and identification of Model Security Problem. It involves Perspective Analytics, Diagnostic Analytics, Predictive Analytics, Detective Analytics and Descriptive Analytics.

  • Pattern Recognition.
  • Anomaly, Intrusion, Incident Detection.
  • Intrusion Response.
  • Statistical Methodology.
  • Malware and Spam Detection.
  • Data Privacy, Risk and Decision Making.
  • Threat Monitoring.

Benefits of Enabling AI in Cyber Security


It involves removal of unnecessary data, Feature Extraction and Selection, Data Cutoff, Parallel Processing, Machine Learning and Deep Learning Algorithms, Result Polling and Optimised Notification.


It involves Alert Correlation, Signature Based Anomaly Detection, Attack Detection Algorithm and combination of Multiple Detection Methods, Algorithm Selection.


It involves Dropped Netflow Detection, Dynamic Load Balancing and MapReduce. Netflow Detection involving Netflow Sequence Monitoring, Netflow Collection, Netflow Storage and Data Analysis, generating warning messages.


It involves Source Data Transformation comprising User Activity, Application Activity, DataBase Activity, Network Activity, Distributed Data Storage, Public Key Infrastructure and Encryption.

Alert Ranking

The pre-processed security event data forwarded to the Data Analysis module, which analyses the data for detecting Cyber attacks. The results of the analysis (i.e., alerts) forwarded to the alert ranking component, which ranks the alerts based on a predefined criterion.

Reliability and Usability

It involves Data Ingestion Monitoring, Maintenance of Multiple Copies, Dropped Netflow Detection and Alert Ranking.

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