Focus area
AI and agentic security
ISO 42001-aligned governance, approval gates and MCP gateway security.
How the gate worksSecurity architect & leader · Bengaluru
Securing AI agents
at enterprise scale.
Hands-on security leader. Ten years from pentesting products to running AppSec, cloud and AI security for a global fintech.
01About
I started as a software engineer at VMware, building the UI for NSX. Then I learned to break things, pentesting products before attackers could. Today I design the security programmes, governance and AI guardrails a global payments company runs on. Offence taught me where systems fail. Leadership is making sure they don't.
I've stood up two security functions from nothing, an enterprise threat-modelling programme and a product security team, and led 17+ architects and engineers. I still work hands-on: threat models and design reviews, security in CI/CD, cloud and Kubernetes hardening, and the MCP gateway controls that keep agentic AI in its lane.
Akash Anand
Security Architect & Leader
AA-2016-SECdrag me
02Impact
0
Years in engineering and security
0
Security functions built from zero
0+
Architects and engineers led
0%
Fewer production security defects in a year
03Expertise
Focus area
ISO 42001-aligned governance, approval gates and MCP gateway security.
How the gate worksA programme teams run themselves.
Security gates built into CI/CD.
CSPM and Kubernetes hardening on AWS, Azure, GCP.
Risk-based, SLA-driven, reported to execs.
Continuous control monitoring, AI management systems included.
Evidence that speeds up deals.
Controls anchored to real attack paths.
Hiring, mentoring, CTFs and training.
04Projects
Posture, baselines and governance across AWS, Azure and GCP.
A secure-by-default Azure foundation with policy guardrails built in.
SAST, SCA, DAST and secrets scanning on every change.
AuthN, tool-level access control and tenant isolation for agentic AI.
Prompt injection, tool abuse and agent privilege, tested with automation.
CTFs, hackathons and training that teach engineers to think like attackers.
05AI security
01 / 05
Purpose, data, model and autonomy. Shadow AI can't be governed until it's visible.
02 / 05
A chatbot over public docs and an agent that moves money get different treatment.
03 / 05
Injection defences, output handling, human-in-the-loop and evals, scaled to the tier.
04 / 05
AuthN, per-tool access, tenant isolation and full logging. Agents get least privilege too.
05 / 05
A recorded decision, runtime monitoring and re-review as models and tools change.
Designed against
06Journey
Bottomline · Global payments and financial technology
NotSoSecure · Claranet for S&P Global
VMware
VMware
07How I work
Every control maps to a real attack path, not a checkbox.
Ship controls that cut real risk in weeks, not quarters, then iterate.
Two security functions built from nothing now run on their own.
Reusable evidence turns customer due diligence into a sales asset.
Continuous control monitoring beats point-in-time audit theatre.
08Credentials
ISC²
In progress · Dec 2026
AWS
AWS
Microsoft
Microsoft
Microsoft
CNCF
CNCF
Proofpoint
Education
KIIT University, Bhubaneswar
9.2CGPA
09Contact
Building a security function or governing AI at scale? I'm up for the conversation.
Prefer a shell?