Mounika Katta

Test Engineer with expertise in automated testing and quality assurance. Possesses a strong background in security operations, including incident detection and response, as well as firewall management. Knowledgeable in cloud security practices, particularly in virtualization and Identity and Access Management (IAM), to safeguard organizational assets.

• Internship in KodNest Technologies.
• Certified with python101 in data science at IBM.
• Certification in python in course era.
• Certification in AWS cloud Practitioner.

• Elected as a class representative in school days & inter college.
• Participated in puzzle Endeavour conducted by ‘MIC COLLEGE OF TECHNOLOGY’ and have won the 1st prize.
• Participated in ‘Tech Treasure Hunt’, ‘Just a Minute’ conducted by ‘MIC COLLEGE OF TECHNOLOGY’.
• Participated in 5km walk for cancer awareness organized by TANA.
• Actively engaged in Workshops & Seminars.

SIEM Integration - Turning the Tide on Security Data Overload

The ever-growing mountain of security data drowns SOC analysts in a sea of information. Security Information and Event Management (SIEM) emerges as a powerful tool, transforming this data deluge into actionable intelligence.

SIEM acts as a central hub, consolidating security data from various sources across the network. This consolidated view empowers analysts to identify trends and patterns that might be hidden in raw data. Beyond aggregation, SIEM employs advanced analytics to correlate seemingly unrelated events, similar to a detective piecing together clues. This correlation helps identify potential security incidents that could otherwise go unnoticed.

In conclusion SIEM integration offers a powerful solution to the growing challenge of security data overload. By consolidating, analyzing, and correlating data, SIEM empowers security analysts to identify threats faster and more effectively. However, successful SIEM implementation requires careful planning and ongoing management. Ultimately, SIEM remains a valuable tool in the ongoing battle to secure our digital world, but it's just one piece of a comprehensive security strategy.

Serverless bulk emailing using AWS Lambda functions, IAM, SES, S3, and Cloud Watch provides a scalable, automated, and manageable solution for sending bulk emails. This approach eliminates the need to manage servers, ensures high email deliverability, and simplifies email data storage and security.

   By leveraging the power of AWS services, organizations can effectively send bulk emails without the complexities of traditional bulk emailing methods. The combination of AWS services provides a robust and scalable solution for organizations of all sizes, ensuring that their email campaigns reach their intended recipients effectively.

Machine learning in cybersecurity involves training algorithms to recognize patterns associated with cyber threats. These models learn from historical data, allowing them to identify known threats and adapt to detect new, previously unseen attacks. This approach enhances traditional security measures by providing dynamic and scalable threat detection capabilities.

Integrating machine learning (ML) into cyber threat detection represents a transformative advancement in cybersecurity. ML empowers security systems to process vast datasets, identify patterns, and detect anomalies with a speed and precision that surpasses traditional methods. This capability enables real-time threat detection, reduces false positives, and enhances the overall efficiency of security operations.

      Despite these challenges, the benefits of ML in cyber threat detection are substantial. As cyber threats continue to evolve in complexity and frequency, ML offers a proactive and adaptive defense mechanism, positioning itself as an indispensable component in modern cybersecurity strategies