Blue Team Bootcamp - Outreach programme in 6 schools

Inauguration of "CYBER CLUB-CYBERTON"

Patent published and filed

Patent published

Resource person at Govt polytechnic college palakkad

Resource Person at PK Das Liberal College of Arts and Science

Certificate of Resource Person at PK Das Liberal College

Resource person at Govt polytechnic college palakkad

Resource person Government women's polytechnic, Nedupuzha

online FDP - " Hands- on Ethical Hacking , OSINT and PenTesting"

NPTEL motivated learner

Dr Geethu Mary George, resource person on “How to Protect Yourself Online” at IEI Thrissur Chapter, Thrissur.

AI Agents Are the New Headcount. Is Your Project Framework Ready? We have been running AI projects with borrowed frameworks Estimation models built for human developers. Testing criteria designed for deterministic software. Governance structures that assume a person is accountable for every output That era is over. When AI agents are active participants in delivery — not just tools — everything changes. Here is what most organisations are not yet pricing in: - [ ] Effort Estimation is no longer straightforward Agent-assisted delivery looks faster on the surface. But the effort shifts, not disappears. Prompt engineering, output validation, agent orchestration, exception handling — these are real project hours that don’t show up in traditional WBS templates. - [ ] Quality Control needs a new vocabulary We are still using pass/fail. AI projects need success rate, confidence level, and consistency across repeated runs. Internal and external quality gates must be redesigned from scratch. - [ ] Cost Models are incomplete Token costs. API call volumes. Model hosting. These are not software licence fees — they scale with usage in ways traditional project budgets don’t anticipate. Add retraining costs post-deployment to avoid model drift, and your TCO calculation looks very different. - [ ] Roles are unrecognisable Developer. Designer. Tester. These titles made sense for deterministic systems. An AI project needs prompt engineers, evaluation specialists, AI safety reviewers, and human-in-the-loop validators. Do we even need a traditional Project Manager — or a new role entirely? - [ ] Governance has a new layer Who owns an agent’s output? How is work recorded? What goes into the audit register when an agent made the decision? Accountability frameworks built for human teams break down fast. - [ ] Manpower costs are higher, not lower AI-skilled resources command 30–50% premium over traditional IT roles. The productivity gains are real — but so is the talent cost. Leaders who budget on headcount reduction alone will be surprised. - [ ] Training never stops This is the part nobody talks about. Model drift happens after go-live. Retraining is not a one-time project cost — it is an ongoing operational expense. Your project plan should reflect this from day one. Here is a useful lens I find helpful: Maturity Level 1 — AI as productivity tool. Copilots, assistants, code helpers. Humans still own delivery. Maturity Level 4 — Agents own delivery. Humans govern, validate, and course-correct. The project framework for Level 1 and Level 4 cannot be the same. Yet most organisations are applying Level 1 thinking to Level 4 ambitions. Many of these questions don’t have clean answers yet. The honest truth is that experience is the only real teacher right now. We are all learning on the job. #AIAgents #AIGovernance #DigitalTransformation #FutureOfWork #BoardroomAI #CXOInsights

NPTEL - course completion