Lead Engineer, Solution Architect, IEEE Senior Member & Enterprise AI Security Researcher.
I build secure, scalable enterprise software platforms and translate AI, security, and privacy research into practical systems that can run in production. My work focuses on enterprise AI governance, secure LLM systems, privacy-preserving AI, cloud architecture, DevSecOps, AIOps, and modernization of business-critical platforms.
About
I am a Lead Engineer and Solution Architect with 15+ years of experience across enterprise software engineering, cloud-native systems, ERP-integrated platforms, AI-enabled workflows, cybersecurity, data privacy, and software modernization.
My work sits at the intersection of engineering execution and applied research: building platforms that are secure, observable, compliant, scalable, and maintainable in real production environments. I regularly comment on how AI, cloud, security, and data systems move from promising prototypes into trusted production capabilities.
Research & technical focus
- Enterprise AI governance, AI security, responsible AI architectures, and secure LLM operations
- Privacy-preserving AI, differential privacy, and minimal audit logging
- Cross-border federated learning governance under HIPAA/GDPR constraints
- Cloud-native observability, AIOps, anomaly detection, and reliability engineering
- DevSecOps, supply chain security, SBOMs, SLSA, provenance, and Zero Trust practices
Media topics I can comment on
For journalists, editors, podcasts, and technology publications looking for practical, engineering-grounded commentary.
I can provide concise commentary on why AI pilots fail to scale, how teams should test non-deterministic AI systems, how organizations can protect data while adopting AI, and what engineering leaders should consider before moving AI-enabled workflows into production.
Article ideas for editors
Potential first-person articles for IT, AI, cloud, security, and software engineering publications.
Enterprise AI Needs Ownership Before Autonomy
Why AI pilots fail when teams do not define monitoring, data risk, support, accountability, and escalation before launch.
AI Coding Tools Are Creating a New Kind of Technical Debt
How AI-assisted development can improve speed while increasing brittle code, hidden security gaps, and maintainability risk.
How to Test AI Agents When Outputs Are Never the Same Twice
Why teams need boundary testing, permission checks, decision-path review, audit trails, and human review for high-risk AI workflows.
Cloud Resilience Is Not Just Uptime
Why resilient cloud systems must be understandable, observable, recoverable, and supportable under real production pressure.
Enterprise leadership
Lead Engineer | Solution Architect
Cornerstone Building Brands
- Architect and lead enterprise platforms integrating ERP systems, pricing intelligence, workflow automation, and data pipelines.
- Design and modernize business-critical systems using .NET, SQL Server, cloud services, APIs, and secure architecture patterns.
- Own production reliability concerns including performance, observability, maintainability, operational controls, and issue triage.
- Translate security, privacy, and governance requirements into deployable engineering solutions.
- Use real-world production experience to evaluate why AI, automation, and modernization efforts succeed only when ownership, data quality, observability, and supportability are designed from the beginning.
Architecture principles
- Security by design: identity, authorization, auditability, least privilege, and secure data flows.
- Operational clarity: observability that supports incident response, accountability, and measurable reliability.
- Cost-aware scale: architectures that balance performance, maintainability, and operational cost.
- Governance-ready delivery: technical decisions documented for audits, stakeholders, and long-term platform ownership.
Selected peer-reviewed and accepted research
Selected published and accepted research focused on AI security, privacy-preserving systems, federated learning, DevSecOps, LLM security, and AIOps.
PrivBuild-AI: An RL-Powered Framework for Differentially Private Data in DevSecOps
Reinforcement learning approach for dynamically adjusting privacy controls while preserving utility in DevSecOps pipelines.
Who Prompted What? Privacy-Preserving Incident Detection for LLM Systems Using Minimal Audit Logs
Minimal-audit logging strategy for detecting enterprise LLM misuse while reducing privacy risk and operational overhead.
Compliance-Aware Cross-Border Federated Learning for Security Telemetry Under HIPAA/GDPR
Governance-aligned federated learning framework for security telemetry collaboration under cross-border policy constraints.
Scalable AIOps: A Framework for Lightweight Observability and Anomaly Detection in Large-Scale Cloud Clusters
Resource-efficient anomaly detection and observability model balancing signal quality, operational cost, and maintainability.
Speaking, chairing & industry sessions
Selected accepted and delivered sessions across AI, cybersecurity, DevSecOps, platform engineering, and software modernization.
3rd International Conference on Artificial Intelligence: Theory and Applications (AITA 2025)
From Pipelines to Predictions: Architecting Data Systems that Fuel Responsible AI
Global Data & AI Virtual Tech Conference Incentives
Next-Gen Pricing Intelligence: Applying Machine Learning to Optimize Complex Enterprise Discounts
9th International Joint Conference on Advances in Computational Intelligence (IJCACI 2025)
Session chair contribution supporting academic discussion and paper presentations in computational intelligence.
Beer City Code
Speaking engagement focused on practical software engineering, secure architecture, and responsible technology delivery.
SecureWorld Detroit
From Scripts to Shields: Automating Cyber Defenses with PowerShell & AI
SecureWorld Virtual Conferences
Automation with PowerShell & dbatools: Streamlining SQL Server Management
MIDOTNET
From Pipelines to Predictions: Architecting Data Systems that Fuel Responsible AI
BSides Cleveland 2025
From Scripts to Shields: Automating Cyber Defenses with PowerShell & AI
Conf42 DevSecOps 2025
Supply Chain Defense by Default — SBOMs, SLSA, and Provenance in CI/CD
Conf42 DevOps 2026
Shift Left, Shield Right: A DevSecOps Playbook for AI-Era CI/CD Pipelines
TestingMind Detroit
Hosted and contributed to panel discussions on AI in quality engineering, automation, and software delivery.
HackOnVibe
Contributed as a judge and mentor, supporting teams building AI-assisted products and evaluating submissions for real-world usefulness, AI integration, technical execution, product thinking, and business potential.
MenderCon 2026
The Hidden Cost of “Just One More Fix”: How Tech Debt Slowly Breaks Production Systems
PHP Tek 2026
Secure by Design – Hardening JavaScript Applications in 2026
Podcasts & media appearances
Conversations on responsible AI, Zero Trust, AI-driven cybersecurity, enterprise architecture, cloud security, and observability.
BizBlend
Can AI make security teams proactive instead of reactive?
AIBiZ
Zero Trust security in hybrid enterprise environments.
The Iferia Techcast
AI, cybersecurity, cloud systems, and practical enterprise technology leadership.
What If You Could
Discussion on AI architecture, practical innovation, and technology leadership.
Impact of AI: Explored
Responsible AI, enterprise systems, and real-world AI adoption.
Web3 Unfiltered
From Firewalls to Intelligence: AI-driven cybersecurity.
DESIGNWAVE Podcast
Recorded conversation pending release.
Conversations with Zena, my AI Colleague
Recorded conversation pending release.
Featured expert commentary
Selected media and industry publications featuring my perspectives on enterprise AI, production readiness, cloud architecture, security, governance, and resilient software systems.
How to Build an AI Development Team: Tips for Success
Expert commentary on building multidisciplinary AI teams, integrating AI into real engineering workflows, and addressing security, privacy, governance, ownership, and production readiness.
Building a Scalable Cloud: Top Tips from the Experts
Commentary on resilient cloud architecture, operational clarity, observability, safe deployments, recovery planning, and avoiding unnecessary architectural complexity.
83 Lessons from Failed or Underperforming Machine Learning Projects
Commentary on production readiness, data quality, ownership, privacy, security, model drift, and the gap between successful AI experiments and reliable production systems.
Peer review & Technical Program Committee service
Served as a Technical Program Committee member and peer reviewer for international conferences and specialized tracks covering artificial intelligence, cybersecurity, networking, cloud computing, privacy, intelligent systems, software engineering, and emerging technologies.
Review responsibilities included evaluating originality, technical correctness, methodology, relevance, presentation quality, strengths, weaknesses, and potential research impact. Totals are current as of July 2026.
View conference reviewing service
Additional professional service
- IEEE Senior Member, Southeastern Michigan Section
- Selected reviewer for OWASP Global AppSec EU 2026
- Contributor and reviewer in security, standards, and AI governance communities
- Conference host, panelist, invited speaker, and session chair
- HackOnVibe judge and mentor for AI-assisted product builders
Review & judging areas
- AI-assisted systems, LLM applications, agentic AI workflows, and applied machine learning
- Cybersecurity, Zero Trust, secure software design, and DevSecOps
- Cloud-native architecture, networking, platform engineering, and observability
- Privacy-preserving AI, federated learning, audit logging, and compliance engineering
- Enterprise software, product feasibility, technical execution, and real-world adoption
Security community & standards contributions
Cloud Security Alliance
Contribution/review activity aligned with Zero Trust, enterprise resilience, and cloud security best practices.
HackOnVibe
Supported hackathon participants through mentorship and judging, with emphasis on practical AI integration, technical quality, product feasibility, security awareness, and user value.
OWASP, AI security, and standards-oriented work
Engagement in AI security and standards discussions related to secure development, AI-generated code, and responsible deployment patterns.
Contact
For media commentary, speaking invitations, podcast appearances, judging opportunities, peer review requests, research collaboration, or enterprise AI/security architecture discussions:
Email: bhaskar.sawant@ieee.org
Location: Canton, Michigan, USA
LinkedIn: bhaskar-bharat-sawant-533218122
Google Scholar: View research profile