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Comprehensive external/internal network penetration test, cloud infrastructure review, and vulnerability assessment for a production hosting ecosystem.
The Objective:
A production hosting provider required a full-scope security audit to evaluate their perimeter defense, verify compliance with the CIS Benchmarks and OWASP Top 10 for Cloud, and uncover critical misconfigurations before public deployment.
The Approach:
I executed a hybrid security assessment combining automated infrastructure reconnaissance with deep manual configuration checks. Every vulnerability was manually validated using tools like Nmap, Wireshark, and CloudBrute to eliminate false positives and scored using standard CVSS 3.1 metrics.
From a single comprehensive audit, I uncovered 31 reproducible findings:
3 High Severity: Exposed database backups publicly accessible via misconfigured AWS S3 buckets, and unauthenticated remote code execution (RCE) vectors on perimeter network appliances.
6 Medium Severity: Overly permissive IAM policies granting excessive root privileges, missing multi-factor authentication (MFA) enforcement on management panels, and outdated SSH protocol versions in use.
10 Low Severity: Unrestricted directory listings on internal staging servers, insecure network daemon configurations, and loose firewall rules permitting unnecessary inbound traffic.
12 Informational: Internal IP address disclosures in HTTP response headers, missing asset tagging, and verbose system version banners.
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What if anyone on your team could ask your data a question and get a live dashboard back in under a minute?
That was the brief. Business users were locked out of their own data. Every question waited on someone who could write SQL, data sat across disconnected systems, and security teams refused to send sensitive records to third-party AI tools.
So we flipped the model. Instead of moving enterprise data to an AI product, we moved the AI analytics product into the customer's AWS account.
What we built:
• Natural-language querying that turns plain-English questions into governed dashboards in under 60 seconds
• Federated queries across SQL and NoSQL sources through Trino
• AI query generation on AWS Bedrock Agents, with Bedrock Guardrails keeping model output in bounds
• Dashboards generated with Apache Superset, plus proactive anomaly and trend alerts
• A white-label React widget that embeds in any product
• Role-based access, row-level security and enterprise SSO enforced at every layer
The result:
Zero data egress. The whole platform deploys inside the customer's VPC and is live on AWS Marketplace.
Building AI features for a data-heavy or regulated product? Let's talk about doing it without your data leaving your cloud.
AI Behavioral Health Documentation Platform
AI-Powered Clinical Documentation Built for Behavioral Healthcare Workflows
Overview
Designed an AI-powered documentation platform focused on real clinical workflows across mental health and addiction treatment. The platform transforms live conversations and uploaded transcripts into structured clinical documentation while using custom knowledge-base logic to maintain domain-specific context.
The architecture was designed around security, scalability, healthcare interoperability, and AI-assisted clinical workflows, providing a foundation for integration with existing EMR systems.
The Challenge
Behavioral health documentation requires more than basic transcription. Clinical notes need to capture nuance, context, treatment terminology, and relevant patient information while fitting naturally into existing healthcare workflows.
The platform needed to:
Convert conversations into structured clinical notes
Understand behavioral health and addiction-treatment terminology
Ground AI outputs in custom clinical knowledge
Support transcript and speech-based inputs
Maintain secure handling of sensitive healthcare data
Provide a scalable foundation for EMR interoperability
AI-Powered Documentation
The system combines speech-to-text, LLM-based summarization, clinical nuance detection, and retrieval-augmented generation (RAG) to transform raw conversations into meaningful documentation.
Custom knowledge-base logic and semantic search help ground generated content in relevant clinical information rather than relying solely on general-purpose model knowledge.
Intelligent AI Architecture
The platform uses an agent-oriented architecture combining:
AI summarization
Clinical context and nuance detection
RAG pipelines
Semantic search
Vector-based knowledge retrieval
Private LLM deployment
LLM fine-tuning capabilities
Custom healthcare knowledge bases
This creates a flexible foundation for specialized behavioral-health documentation workflows.
Healthcare-Ready Infrastructure
Security and interoperability were considered at the architecture level, including:
HIPAA-ready cloud architecture
Encryption at rest
Secure backend infrastructure
PostgreSQL + pgvector
Healthcare interoperability support
HL7/FHIR integration readiness
EMR integration support
Serverless backend architecture
Technology Stack
Frontend: React, Vite
Backend: Python, FastAPI
AI/LLM: AWS Bedrock, Anthropic Claude, AI Agents
AI Infrastructure: RAG, Vector Database, pgvector, Semantic Search, LLM Fine-tuning
Database: PostgreSQL
Healthcare: HL7/FHIR, EMR Integration
Cloud & Security: AWS, HIPAA-ready architecture, Encryption at Rest
Speech: Speech-to-Text APIs
The Result
A scalable AI clinical documentation platform built specifically around behavioral healthcare workflows—combining conversational AI, clinical knowledge retrieval, secure infrastructure, and healthcare interoperability into a unified system.
therapists lose so many hours on notes, this solves a real problem
Two AI coding agents, two machines, one repo. Who touches what?
Parallel agents step on each other: overlapping edits, rewritten modules, a working tree nobody trusts.
AlfaNest Relay is a local-first coordination bus for AI coding agents.
Agents lock a path, publish one patch per finished task, and the other side receives exports and impact first.
Apply is a human YES or NO. AUTO apply is off, and a reject leaves the disk untouched.
The relay carries events and diffs, never the project tree. .env and secret globs never leave.
The open-source repo (MIT) is the MCP plugin: Streamable HTTP, OAuth sign-in, manifests for Cursor, Claude Code, Codex, VS Code and Gemini CLI.
#MCP #AIAgents #DeveloperTools #LocalFirst #OpenSource #AICoding #DevSecOps
