97 lines
5.1 KiB
Markdown
97 lines
5.1 KiB
Markdown
# hncb-fusion-deid-demo
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Demo of the HNCB use case: a **reversible PII de-identification round trip**
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through **Axway Amplify AI Gateway (Fusion)**, with the reasoning done by an
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**Amazon Bedrock AgentCore** agent. The advisor types a query containing a real
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name; the cloud only ever sees a token; the real identity is restored before the
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answer is shown.
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> **Working with Claude Code? Read `CLAUDE.md` first** — it has the context,
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> commands, and task backlog.
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> **Status: unrun scaffold.** Authored, not executed. Review everything, pin
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> dependency versions, and adjust AgentCore specifics to the current CLI.
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> Demo-grade, not production-grade.
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## Fusion is shared SaaS
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Fusion is **not deployed by this repo** — it's a shared Amplify AI Gateway SaaS
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instance, configured in its console (`fusion/POLICY_SETUP.md`). Because it's SaaS,
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anything it calls must be a **public HTTPS endpoint with auth**. So the AWS side's
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job is to expose two endpoints Fusion calls — **`/tokenize`** (ingress) and
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**`/restore`** (egress) — plus host the detector, vault, RAG tool, and agent.
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Those two endpoints are the main remaining build (tasks T1/T2 in `CLAUDE.md`).
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## What it demonstrates
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Raw PII (a Chinese name + a Taiwan ROC ID) is detected on ingress, replaced with a
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**random, format-safe token**, and only the tokenized prompt goes to the cloud
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agent. The agent tool-calls back "on-prem" with the token, gets a de-identified
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evidence package, and writes talking points. Fusion restores the identity on the
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way out. **The money shot:** show the Bedrock request / AgentCore trace live — the
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cloud only ever saw `CUST_000123`, never `王小明`.
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## Trust zones are logical
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Everything is one AWS account. The "on-prem" zone is tag-labelled resources
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(`Zone = on-prem-VPC-A`) standing in for HNCB's branch data centre. Proves
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data-flow behaviour, not physical residency — say so on camera.
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## Component → service map (their 8 steps)
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| Step | Component | Service in this repo |
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|---|---|---|
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| 1, 8 | Advisor UI | `ui/index.html` on S3+CloudFront (or local) |
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| 2, 3-route, 8 | **Fusion AI Gateway** (the product) | **shared SaaS** — configured via `fusion/POLICY_SETUP.md` (not deployed) |
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| 2 (ingress), 8 (egress) | `/tokenize` + `/restore` endpoints Fusion calls | **TODO** `gateway_api/` (tasks T1/T2) |
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| 2 | PII **detector** (typed findings, not redaction) | Presidio on Fargate — `presidio/` |
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| 2, 4, 8 | Token **vault** (reversible map) | DynamoDB — `terraform/main.tf` |
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| 3, 7 | Cloud **agent** + model | AgentCore Runtime + Bedrock — `agent/` |
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| 4 | Tool bridge (Lambda → MCP tool) | AgentCore Gateway — `scripts/agentcore_setup.sh` |
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| 4-6 | On-prem **RAG tool** + data | Lambda + DynamoDB — `lambda_rag/`, `seed/` |
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| all | Observability | AgentCore Observability + CloudWatch |
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## Repo layout
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```
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CLAUDE.md start here if using Claude Code (context + task backlog)
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terraform/ DynamoDB (vault + customers), RAG Lambda, IAM, Presidio hosting
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lambda_rag/ RAG tool: token resolve -> de-identified evidence package
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gateway_api/ TODO: /tokenize + /restore endpoints Fusion SaaS calls (T1/T2)
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agent/ Strands agent for AgentCore Runtime + tool schema
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presidio/ PII detector service (typed findings) + Dockerfile
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seed/ fake customer (Wang Xiaoming) + seed script
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ui/ advisor UI (restored-vs-tokenized split view)
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scripts/ deploy.sh, agentcore_setup.sh, teardown.sh
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fusion/ POLICY_SETUP.md (SaaS console config — the manual part)
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```
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## Prerequisites
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`aws-cli` configured (creds + region), `terraform >= 1.5`, `docker`, `python3`,
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the AgentCore CLI (`npm i -g @aws/agentcore`), Bedrock model access enabled for
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`bedrock_model_id`, and access to the shared **Fusion SaaS** instance.
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## Deploy
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```bash
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bash scripts/deploy.sh # infra -> presidio image -> ECS -> seed -> AgentCore
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# then: build /tokenize + /restore (gateway_api/, tasks T1/T2)
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# then: configure the shared Fusion SaaS instance (fusion/POLICY_SETUP.md)
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# then: point ui/index.html GATEWAY_URL at the Fusion SaaS entrypoint and open it
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```
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## Demo script (maps to the 8 steps)
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1. Advisor UI: submit *"請幫我整理王小明最近三個月的理財往來，並給我下次拜訪話術。"*
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2. Fusion (via `/tokenize`) detects `王小明` + `A123456789`, tokenizes, logs tokens only.
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3. Show the Bedrock/AgentCore trace — the prompt the cloud saw contains `CUST_000123`.
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4-6. Agent tool-calls back on-prem; RAG resolves the token, returns a summary.
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7. Agent writes talking points (no PII).
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8. Fusion (via `/restore`) restores `王小明`; the UI shows restored beside tokenized.
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## Teardown
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```bash
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bash scripts/teardown.sh # stop paying for Fargate / AgentCore
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```
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## Honest caveats
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- **zh-TW detection is demo-narrow** — tuned to the scripted entities, not
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production recall. That remains the real-engagement risk.
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- **Per-request randomization** lives in the `/tokenize` mint step; confirm it
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satisfies HNCB's "different each time" requirement.
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- **The agentic token-resolution loop** (agent tool call → on-prem RAG resolves the
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token) is custom orchestration by design — where Fusion's depth is the argument.
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