Institutional Memory Agent Server

A Splunk-native agent that turns SOC analyst reasoning into a queryable institutional knowledge graph, accessible from SPL, a Python CLI, and a Model Context Protocol server.

Why

Existing SIEM tooling captures events. Nobody captures analyst reasoning. When a senior SOC analyst leaves, their mental model - which alerts are owned by scheduled batch jobs, which executives travel internationally, which subnets host sanctioned pentests - leaves with them. ima watches what analysts decide and why, clusters those decisions through a security-tuned LLM, and serves the resulting institutional memory back as a first-class data surface inside Splunk.

What it actually does

analyst closes alert     →   ima asks "why?" (10-second prompt)
                              ↓
                          annotation lands in KV Store (ima_annotations)
                              ↓
        | imabuild  →   clusters by (event_type, disposition)
                          and calls Foundation-Sec-1.1-8B
                              ↓
                          structured knowledge entry in ima_knowledge
                              ↓
new analyst asks         →   | imaquery question="finance Monday"
"what do we know         →   returns: "Finance batch job triggers failed-auth
about X?"                       bursts every Monday 6am."  conf=1.0  ×3 evidence

Quick start (collaborators - read this first)

Prereqs: Python 3.10+, Splunk Enterprise running locally on :8089, Ollama for the local LLM.

git clone <repo>
cd Splunk_agentic_ops
.\bootstrap.ps1                       # Windows. macOS/Linux: ./bootstrap.sh
notepad .env                          # paste your SPLUNK_TOKEN (see below)
.\.venv\Scripts\Activate.ps1
ima auth check
ima kv init
ima demo seed --clear
ima knowledge build                   # ~3 min on CPU
ima knowledge query "finance"

To get a Splunk auth token: Splunk Web → Settings → Tokens → New Token. User: admin, audience: anything, expires: 90+ days.

The bootstrap script creates .venv, installs the CLI in editable mode, copies .env.example to .env, and pulls the Ollama model if it isn't already.

Install the Splunk app

The CLI is the dev harness. The Splunk-native deployment lives at splunk_app/ima/. There's a helper script that handles elevation, copy, and restart in one shot - open an Administrator PowerShell, cd into the repo, and run:

.\install_splunk_app.ps1

(Manual equivalent if you prefer: Copy-Item -Recurse -Force ".\splunk_app\ima" "C:\Program Files\Splunk\etc\apps\"; & "C:\Program Files\Splunk\bin\splunk.exe" restart)

After restart, open Splunk Web → Apps → Institutional Memory Agent for the dashboard, and try the custom search commands directly:

| imaquery question="finance"
| imaaboutasset asset="acct-prod-01"

| imaannotate alert_id="NOTABLE-2024-09-21" disposition="false_positive" `
              reason="Finance batch job again, Monday 6am" `
              asset="acct-prod-01" event_type="failed_auth_burst"

| imabuild

Repo layout

.
├── ima/                          # Python CLI (dev tool)
│   ├── cli.py                    #   Typer entrypoint
│   ├── config.py                 #   .env loader
│   ├── splunk_client.py          #   splunk-sdk Service
│   ├── kvstore.py                #   KV Store helpers
│   ├── llm/foundation_sec.py     #   Ollama / Splunk-hosted client
│   └── commands/                 #   auth, kv, alerts, knowledge, demo
├── splunk_app/ima/               # Splunk app (production deployment surface)
│   ├── bin/                      #   3 custom search commands
│   ├── default/                  #   collections, commands, transforms, dashboard XML
│   └── README.md
├── bootstrap.ps1 / bootstrap.sh  # one-command collaborator setup
├── pyproject.toml                # installs the `ima` console script
├── .env.example                  # config template; .env is gitignored
├── ARCHITECTURE.md               # data model, design choices, why-not-SOAR
├── LICENSE                       # MIT
└── README.md                     # this file

Expose IMA to external AI agents (MCP)

The same knowledge graph is available as Model Context Protocol tools so any MCP client - Claude Desktop, SAIA Agent Mode, custom agents - can query institutional memory natively.

Four tools are exposed: query_knowledge(question), record_annotation(alert_id, disposition, reason, ...), list_recent_annotations(limit), build_knowledge().

Run as a stdio server (for Claude Desktop / IDE clients):

ima mcp serve

Run as HTTP (for remote autonomous agents):

ima mcp serve --http --port 8765

Claude Desktop config - edit %APPDATA%\Claude\claude_desktop_config.json and add:

{
  "mcpServers": {
    "ima": {
      "command": "C:\\Users\\shmishra\\Documents\\Splunk_agentic_ops\\Splunk_agentic_ops\\.venv\\Scripts\\python.exe",
      "args": ["-m", "ima.cli", "mcp", "serve"]
    }
  }
}

Restart Claude Desktop; the IMA tools become available in any conversation. Ask the agent "what does the SOC know about acct-prod-01?" and it'll call query_knowledge for you.

How it uses Splunk's AI stack

Splunk surface	How ima uses it
KV Store	Three collections - ima_annotations, ima_knowledge, ima_assets - declared in splunk_app/ima/default/collections.conf and used as the persistence layer for the knowledge graph.
Custom Search Commands (Python SDK 3.0)	| imaannotate, | imabuild, | imaquery - first-class SPL commands so any saved search, dashboard, or analyst can trigger IMA.
Foundation-Sec-1.1-8B	The extraction prompt + JSON schema target the Splunk-hosted Foundation-Sec model. Local dev runs against an Ollama-hosted Llama-3.1-8B stand-in (no GPU on the dev box); swap to the Splunk-hosted endpoint via a one-line .env change.
Simple XML dashboards	ima_overview.xml gives the SOC a single pane: contributor stats, disposition mix, knowledge table, and an interactive "ask the agent" input.
MCP Server	A standalone Python MCP server in ima/mcp_server.py exposes four tools (query_knowledge, record_annotation, list_recent_annotations, build_knowledge). Run with ima mcp serve - Claude Desktop, SAIA Agent Mode, and any MCP client can query institutional memory natively.

See ARCHITECTURE.md for the full design.

Why this isn't a SOAR playbook

SOAR automates actions - block this IP, isolate this endpoint. IMA captures and queries reasoning - why did the senior analyst close this kind of alert as a false positive last quarter? Complementary surfaces, not substitutes.

Authors

• Shiwani Mishra
• Saurabh Gupta

Both contributed equally to the design and implementation.

License

MIT - see LICENSE.