Integrated API-driven architectures and optimized databases for performance and security. Conducted web penetration testing to identify vulnerabilities and strengthen platform security. Collaborated with cross-functional teams to enhance feature integration, UX/UI, and overall product performance.

Led full product lifecycle from requirements to sprint planning, ensuring on-time delivery.
Collaborated with cross-functional teams for seamless feature integration, driving product enhancements and optimizing UX/UI with Figma. Championed Agile practices, improving product positioning and user engagement through market research.

Developed and implemented custom detection rules within Zoho SIEM to analyze security events, identify potential indicators of compromise (IOCs), and enhance the organization’s threat detection capabilities. Conducted detailed investigations into suspicious activities and anomalies flagged by the SIEM, performing root cause analysis to pinpoint the source of threats. Regularly updated detection rules and fine-tuned the SIEM configuration based on the latest security research and organizational needs, increasing accuracy and reducing false positives. Designed AI-driven SOAR tools and a custom SIEM solution, reducing incident response and threat detection times by 30% while enhancing security and user trust.

Conducted comprehensive penetration tests across the organization’s infrastructure, including networks, web applications, and wireless networks to identify vulnerabilities and assess overall security posture. Utilized industry-standard tools like Metasploit, Nmap, Burp Suite, Wireshark, and AngryIP Scanner to simulate real-world attacks, attempting unauthorized access to systems, sensitive data, and critical resources. Assessed the efficacy of existing security controls by exploiting discovered vulnerabilities, providing actionable insights for strengthening defenses against potential threats. Delivered detailed reports outlining identified vulnerabilities, risk levels, and recommended remediation strategies to stakeholders and technical teams.

Performed API security assessments and penetration testing to identify vulnerabilities and improve security posture. Created a detailed report with identified flaws and mitigation strategies to enhance API security.

Many APIs suffer from authentication weaknesses, broken access controls, and excessive data exposure. This project focused on finding security gaps through penetration testing and providing remediation strategies to protect against real-world threats.

<b>Tools Used:</b>&nbsp;Burp Suite, Postman, OWASP ZAP, Nmap, Metasploit

Conducted a Penetration security assessment to identify vulnerabilities and improve security posture. Created a detailed report outlining security flaws.

Penetration Testing Assessment & Mitigation Report

This case study details a project focused on automating the manual data entry process for SOC analysts responding to security incidents.  My role was to design and implement an automated solution to reduce the time analysts spent on administrative tasks, allowing them to focus on higher-priority alerts and improving overall response times. The project ran from October 1, 2023 to November 15, 2023.

SOC analysts were spending significant time manually filling out incident response forms. This process was time-consuming and diverted their attention from critical incident handling.  This inefficiency impacted overall response times and the effectiveness of incident mitigation.  Internal data showed an average of 20 minutes spent per incident on manual data entry.

The primary objective was to reduce the time spent on manual data entry by at least 30%.  Success would be measured by tracking the average time spent per incident on data entry and comparing it to the pre-automation baseline.  Secondary metrics included analyst feedback on the tool's usability and impact on their workflow.

The strategy involved developing a Python-based automation solution that integrated with our existing ticketing system and the incident response forms. The solution would automatically populate the forms with data extracted from the tickets.  The roadmap involved initial design, API integration, testing, and deployment.  We used a phased rollout to minimize disruption and ensure smooth integration.

We conducted user interviews with SOC analysts to understand their current workflow and challenges. This informed the design and functionality of the automation tool. We also performed thorough API documentation review and testing to ensure seamless data extraction and transfer.  Before full deployment, we conducted beta testing with a subset of analysts to gather feedback and make necessary adjustments.

The development process followed an Agile methodology, with two-week sprints. Daily stand-ups and sprint reviews ensured effective communication and progress tracking. We used Jira for task management and Confluence for documentation.  Feature prioritization was based on analyst input and impact on response time. We prioritized core functionality and iteratively added enhancements based on feedback.

Development proceeded as planned, with minor adjustments based on beta testing feedback.  We encountered some initial challenges with data formatting inconsistencies between the ticketing system and the incident response forms. These were resolved through careful data transformation within the Python script. Regular updates were communicated to stakeholders through Jira and email.

The primary challenge was integrating with the ticketing system's API due to its complexity and lack of comprehensive documentation.  We mitigated this by close collaboration with the IT team who provided additional support and clarifying information.  Another challenge was ensuring data integrity and preventing errors during automated data transfer.  This was addressed by implementing robust error handling and data validation within the script.

The launch involved a phased rollout to SOC analysts, starting with a small group for further testing before wider deployment.  Training was provided to familiarize analysts with the new system.  The go-to-market strategy focused on internal communication and showcasing the tool's efficiency benefits through success metrics.

The automation solution exceeded expectations, achieving a 35% reduction in average time spent on data entry per incident.  Analyst feedback was overwhelmingly positive, highlighting the tool's ease of use and its impact on productivity.  This allowed analysts to dedicate more time to high-priority tasks and improve overall incident response times.

The project demonstrated the value of automation in streamlining workflows and improving efficiency. Key learnings include the importance of thorough API documentation, robust error handling, and user feedback throughout the development process.  The project strengthened my API integration skills and honed my problem-solving abilities.

The successful automation of incident reporting has significantly improved the efficiency of our SOC team.  Future iterations will include enhanced reporting capabilities, improved error handling, and integration with other security tools. This project serves as a model for future automation initiatives within the organization.

Streamlining SOC Analyst workflow through automation to reduce incident response times and improve efficiency.

Automating SOC Incident Response

This project aimed to revolutionize our Security Operations Center (SOC) by implementing AI-powered Security Orchestration, Automation, and Response (SOAR) technology.  My role as a Product Manager was to lead the initiative, focusing on enhancing efficiency and improving response times to critical security incidents. The project ran from January 2023 to November 2023 and involved a cross-functional team including SOC analysts, engineers, and AI specialists. We leveraged Jira for project management, utilizing agile methodologies for development and deployment. The target audience consisted of SOC analysts and security operations managers who faced challenges managing duplicate tickets and known vulnerabilities, leading to delayed responses to high-priority alerts.  The goal was to free up analyst time for critical tasks through automation.

Our SOC analysts were spending too much time on repetitive, low-priority tasks like addressing duplicate tickets and known vulnerabilities. This inefficiency resulted in delayed responses to critical security alerts, impacting our overall security posture.  Data analysis revealed that a significant portion of analyst time (approximately 40%) was dedicated to these low-impact tasks. This directly translated to a slower response time to high-priority incidents, increasing the risk of security breaches and impacting our ability to meet our service level agreements (SLAs).

The primary objective was to reduce the time spent on repetitive, low-priority tasks by at least 50%, freeing up analysts to focus on high-priority alerts.  Secondary objectives included improving overall ticket resolution time and increasing analyst satisfaction.

Success was measured by monitoring several key performance indicators (KPIs):

*   Ticket resolution time (average time to resolve a ticket)
*   Percentage of tickets automatically handled by the SOAR system
*   Analyst availability for high-priority alerts
*   Analyst satisfaction (measured through surveys)
*   Reduction in the number of high-priority incidents


Our strategy involved a phased approach, starting with automating the handling of duplicate tickets and known vulnerabilities.  We prioritized features based on their impact and feasibility, using a MoSCoW method to manage scope.  The roadmap included detailed milestones, such as identifying suitable SOAR tools, integrating the chosen technology into our existing SOC workflows, testing, and finally, deploying the solution to production.

Extensive market research was conducted to identify and evaluate suitable AI-powered SOAR platforms. We considered factors such as scalability, integration capabilities, and ease of use.  We also analyzed competitor solutions and industry best practices to inform our decision-making process. User research involved interviews and surveys with SOC analysts to understand their workflows and pain points, ensuring that our solution directly addressed their needs.

Ideation sessions involved brainstorming potential solutions, evaluating different SOAR tools, and designing workflows to optimize automation.  We collaborated closely with engineers and AI specialists to ensure the technical feasibility and scalability of our solution.  Agile methodology, with two-week sprints, guided our development process. Daily stand-ups, sprint reviews, and retrospectives ensured transparency, fostered collaboration and allowed for iterative improvements.

The project was executed as planned, with regular progress updates shared with stakeholders.  We implemented a staged rollout, starting with a pilot program involving a subset of analysts. This allowed for early identification and resolution of any issues.  The feedback collected during the pilot program was invaluable in refining the solution and ensuring a smooth transition to full deployment.

Integrating the AI-powered automation into our existing ticketing system presented a significant technical challenge.  We mitigated this by working closely with the engineering team to develop custom integrations and by thoroughly testing the solution in a staging environment before deploying it to production.  Another challenge was ensuring that the AI algorithms were accurate and effective in identifying duplicate tickets and known vulnerabilities. We addressed this through continuous model training and refinement using feedback from the SOC analysts.

The launch involved training SOC analysts on the new system and providing ongoing support.  We conducted thorough beta testing before the full rollout to minimize disruption and ensure user satisfaction.  Communication was critical during this phase, and we provided regular updates and feedback loops to all stakeholders.  The go-to-market strategy involved showcasing the positive impact of the solution on SOC efficiency, focusing on metrics such as reduced ticket resolution time and increased analyst productivity.

The project resulted in a significant reduction in ticket resolution time by 60%, a 75% decrease in the time spent on duplicate tickets, and a 55% increase in analyst availability for high-priority alerts.  Analyst satisfaction improved by 40%, as measured by post-implementation surveys.  The quantitative results clearly demonstrated the success of the project in enhancing SOC efficiency and improving overall security posture.

What went well:  The phased rollout approach and continuous collaboration with the SOC analysts were instrumental in the project’s success.  What could be improved:  More robust change management processes could have helped manage the transition more smoothly.  Continuous model training and monitoring will be crucial to maintain the effectiveness of the AI algorithms.  These learnings will shape future projects by emphasizing early and continuous user feedback and improving our change management processes.

This project successfully enhanced SOC efficiency by leveraging AI-powered SOAR technology. The positive results demonstrate the potential of automation in optimizing security operations and improving response times to critical incidents.  Future iterations will focus on extending automation to other areas of SOC operations, expanding the capabilities of the AI algorithms, and further refining the user experience.

Improving SOC productivity and critical incident response by automating repetitive tasks using AI-powered SOAR technology.

AI-Powered SOAR for Enhanced SOC Efficiency

Project Name: Scheduled Exports in Zoho Cliq

Manual exports are time-consuming and prone to errors, and third-party tools may not integrate well or introduce security risks. The Scheduled Exports feature aims to automate data exports, ensuring compliance, enhancing efficiency, maintaining data security, and offering easy access to historical data.

In conclusion, the Scheduled Exports feature for Zoho Cliq will automate and streamline the export of chat data, providing essential benefits such as compliance with data retention policies, enhanced operational efficiency, and robust data security. By offering flexible scheduling and various delivery options including email, cloud storage integration with Zoho WorkDrive (with future plans for third-party services), and secure download links, this feature will significantly improve user experience and data management capabilities for enterprises, project managers, and IT administrators.

The Scheduled Exports feature in Zoho Cliq allows users to automatically export their chat data at specified intervals. This functionality is designed to meet the needs of users requiring regular backups for compliance, record-keeping, or analysis purposes.

Scheduled Exports in Zoho Cliq

This case study details a project focused on increasing next-day retention for Google News by improving personalization, credibility, and efficiency in news consumption. My role was to define the product vision, align stakeholders, and oversee feature development to drive engagement. The project ran from February 2025 to July 2025.

 Users were encountering irrelevant news recommendations, leading to lower engagement and retention. Trust in news sources was low due to the prevalence of opinion-based content, and long-form articles were overwhelming for time-constrained users.

The strategy focused on three core improvements:

The roadmap included research, feature design, phased implementation, and impact measurement to ensure smooth adoption.

Research and Validation
User research, including surveys and interviews, identified key pain points around relevance and trust. A/B testing validated the impact of personalization features, and prototype feedback sessions refined the user experience before launch.

Product Development Process
Following Agile methodology, we conducted two-week sprints, tracking progress in Jira and documenting insights in Confluence. Prioritization was guided by user impact and feasibility, ensuring high-value features were delivered early.

Execution and Delivery: Development proceeded in phases, with incremental feature releases. The team overcame data integration challenges by optimizing AI models and refining feed algorithms based on real-time engagement data.


The improvements in retention and engagement validate the impact of personalized and credible news delivery. Future iterations will enhance AI explainability, deeper integration with Google Assistant for voice-based summaries, and expanded multilingual support to cater to a broader audience. This project serves as a foundation for scaling AI-driven content personalization across other Google products.

When a user clicks a news article in Google News, a pop-up shows a Gemini AI-generated summary from the original source, with a link to the full article.

Google News Article Summary Pop-Up

Traditional AI chatbots often depend on cloud infrastructure, raising privacy concerns and requiring internet connectivity. This project focused on building a self-hosted AI assistant capable of real-time responses without external dependencies.

Developed a local AI chatbot powered by Ollama, designed for offline conversational AI.

Hey there 👋, I am Shiyam Sundar!