Cybersecurity ConsultantCloud Security & DevSecOps Strategist

Problem

Need to integrate security scanning into development workflows without slowing down deployment cycles.

Approach

Implemented automated security scanning in GitHub Actions workflow with SAST, dependency scanning, and container image scanning.

Tools

Outcome

Reduced security vulnerabilities in production by 60% and enabled shift-left security practices in the development lifecycle.

Business Impact

Enabled shift-left security that reduced production risk and ensured regulatory compliance for enterprise-scale deployments.

Key Learning

Security automation in CI/CD requires balancing thoroughness with speed. Parallel scanning stages and caching strategies are critical.

Problem

Enterprises adopting AI/LLM tools lack standardized governance processes, exposing them to data privacy, compliance, and operational risks.

Approach

Designed and authored a comprehensive AI Intake SOP framework covering risk classification, data handling requirements, vendor assessment criteria, and approval workflows for enterprise AI tool adoption.

Tools

Outcome

Created a repeatable governance framework that enables organizations to evaluate and onboard AI tools with consistent risk controls and audit trails.

Business Impact

Reduced unvetted AI tool proliferation and established a scalable governance process that aligns AI adoption with enterprise risk appetite and regulatory requirements.

Key Learning

Effective AI governance requires balancing innovation velocity with risk controls—overly restrictive policies stifle adoption, while lax governance creates unacceptable exposure.

Problem

Manual security assessments of AI/LLM deployments are time-intensive and inconsistent, leaving gaps in coverage across enterprise AI portfolios.

Approach

Built an automated security scanning tool that evaluates AI/LLM applications against common vulnerability patterns including prompt injection, data leakage, model manipulation, and insecure API configurations.

Tools

Outcome

Automated the detection of critical AI security vulnerabilities, enabling continuous assessment across multiple AI deployments with consistent risk scoring.

Business Impact

Enabled proactive risk mitigation for AI deployments, reducing manual assessment overhead and providing leadership with quantifiable risk metrics for informed decision-making.

Key Learning

AI security scanning must evolve with the threat landscape—static rule-based checks are insufficient; adaptive testing frameworks that account for model-specific behaviors are essential.

Problem

Need foundational understanding of AWS cloud services and security best practices for cloud-native deployments.

Approach

Completed AWS Cloud Foundations certification covering core services, security models, and architectural patterns.

Tools

Outcome

Certified knowledge of AWS core services and security best practices, enabling secure cloud architecture design.

Key Learning

Cloud security starts with proper IAM policies and network segmentation. Understanding shared responsibility model is crucial.

Problem

Need practical understanding of security operations, incident response, and defensive security strategies.

Approach

Completed comprehensive course on security operations, SIEM usage, threat detection, and incident response procedures.

Tools

Outcome

Developed hands-on skills in threat detection, log analysis, and incident response workflows.

Key Learning

Effective blue team operations require continuous monitoring, proper log aggregation, and well-defined incident response playbooks.