Cybersecurity is one of the many areas where Generative Artificial Intelligence has many uses. By developing predictive models, producing simulated environments, and analyzing massive amounts of data, GenAI can fundamentally change cybersecurity, including cloud-based devices, and allow for the early detection and mitigation of attacks.
Digital Risk Management
Identity and Access Management
Endpoint Detection and Response
Cloud Access Security Brokers
Spam Prevention and Phishing Blocking
Behavioural Analysis
Cyber Threat Analysis and Model Security Solutions for Enterprises
Security Analytics to understand and detect risk level of vulnerabilities. Artificial Intelligence, Machine Learning, and Deep Learning techniques to reduce human error. Identify security breaches and eliminate obstructions.
It involves Network Security, Cloud Security, IoT Security, Malware, and Autonomous Security. Operational Management and elimination of security issues, understand risks and threats to the internal system.
It includes analyzing advanced threatening behavior, evolving internal architecture to scan system deficiencies. Data and Application Security involves Security Analytics, Threat Prediction, Spam Detection, and Data Privacy.
Enabling AI to recognize patterns for detecting and mitigating security vulnerabilities
The remote work trend and the rise of connected endpoints have created many cybersecurity challenges. Modern AI-driven endpoint response and detection can proactively block and isolate malware and ransomware threats.
It involves Alert Correlation, Signature-Based Anomaly Detection, Attack Detection Algorithm, and 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.
The pre-processed security event data is forwarded to the Data Analysis module, which analyses the data for detecting threats beforehand. Alerts are sent to the threat mitigation team for instant action.
It involves Data Ingestion Monitoring, Maintenance of Multiple Copies, Dropped Netflow Detection, and Alert Ranking.
Cloud-native security differs from traditional security in that it requires a comprehensive approach to security and observability at all stages of workload deployment—build, deploy, and runtime.
13 April 2023
Find out how Generative AI aid in the detection of cyber threats and the protection of systems from the human error and other vulnerabilities that lead to breaches.
Thanks for submitting the form.