This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content
Cyber Autopsy is a cyber-forensics investigation room for asking structured
questions about an incident: what happened, what came before it, which evidence
supports it, what remains unknown, and what was knowable at a specific point in
time.
The project started as a Kaggle-style benchmark for evaluating whether an AI
can reconstruct an attack from fragmented reports without presenting guesses as
facts. I added Sanity as the structured content layer, an MCP-compatible
investigation server, and a forensic-themed web interface.
The core model preserves the relationships that make an investigation useful:
Evidence ──supports──> Event ──precedes/enables/causes──> Event
Evidence ──supports/contradicts──> Claim
The UI presents each case as a small investigation console. Investigators can
select a case, read its incident description, ask a question, and inspect the
result as a reconstruction, relationship graph, uncertainty list,
contradictory claims, and expandable evidence with source provenance.
Cybersecurity autopsy is an evidence-reconstruction problem. Analysts rarely
receive one perfect timeline; they receive authentication logs, endpoint
observations, threat reports, analyst notes, negative findings, and competing
claims. The difficult part is deciding how those pieces relate without
overstating what they prove.
Cyber Autopsy uses Sanity to make that reasoning inspectable. An analyst can
follow an event back to its supporting evidence, move through the attack graph,
check whether a claim is contradicted, and distinguish confirmed, inferred,
attempted, and unknown. This is useful for:
The result is closer to a digital incident autopsy than a generic search box:
the system preserves the chain of evidence, the gaps in the record, and the
reasoning boundaries around each conclusion.
Run the project locally:
npm run dev
Then open http://localhost:3000.
The repository contains the Python benchmark, Sanity Studio, importer, MCP
server, local UI, tests, and documentation.
Repository: https://github.com/ujjavala/cyber-autopsy
The most relevant implementation files are:
studio-cyber-autopsy/schemaTypes/index.ts Sanity content model
scripts/sanity-seed.mjs repeatable JSONL importer
agent/sanity-context.mjs scoped GROQ investigation context
agent/mcp-server.mjs MCP tool surface
web/app.js investigation UI behavior
web/style.css cyber-forensics console theme
Sanity gave the project a durable, queryable content layer for the benchmark's
investigation graph. Before the integration, the dataset was primarily a group
of local JSONL files. That was reproducible, but it made the content harder to
inspect, connect, and reuse across an agent, a Studio, and a browser UI.
With Sanity, the same investigation content is modeled as linked documents.
Evidence, events, claims, relationships, incidents, and sources can be queried
independently or traversed together. This helped in four practical ways:
Sanity therefore acts as the investigation knowledge base, while the context
layer acts as the reasoning boundary. The agent is not asked to remember an
incident or invent a timeline; it queries structured content and returns a
calibrated reconstruction.
Instead of storing one large incident document, I modeled the investigation as
separate document types. This matches the way a forensic analyst works: an
incident is the case context, evidence is an observation, an event is a
reconstruction, a claim is an assertion, and a relationship explains how two
events connect.
The separation gives each kind of information a clear validation and query
boundary. For example, an event has a status and confidence, while an
evidence document has a timestamp, observation type, entities, and source
references. A relationship has a source event, target event, relation type,
and supporting evidence.
The most important Sanity feature here is the reference field. Evidence does
not copy an event's text, and a relationship does not copy both event records.
They point to the canonical documents:
defineField({
name: 'sourceEvent',
type: 'reference',
to: [{type: 'event'}],
validation: (rule) => rule.required(),
})
defineField({
name: 'evidence',
type: 'array',
of: [defineArrayMember({type: 'reference', to: [{type: 'evidence'}]})],
})
This turns Sanity into a navigable investigation graph. The agent can traverse
case -> incident -> event -> evidence -> source, then separately retrieve
relationship and claim documents for the same incident. In the UI, that
becomes a reconstruction with visible provenance instead of an unsupported
paragraph.
The Studio schema validates required identifiers and references, constrains
confidence to the range 0..1, and uses controlled options for evidence and
event status. The available status taxonomy is deliberately forensic:
confirmed · inferred · attempted · failed · unknown
Claims additionally support contradicted. These values are used in the UI to
separate established facts from uncertain or failed steps. This prevents a
missing observation from silently becoming a confirmed event and makes
calibrated uncertainty part of the content model.
The context layer uses GROQ projections to request only the fields required by
an investigation. The -> operator dereferences related documents so one
response can contain the case, incident summary, evidence, and source metadata
without the browser making a request for every individual record.
For example, the case query projects the incident and joins its evidence and
events:
*[_type == "investigationCase" && caseId == $caseId][0] {
caseId, mode, temporalCutoff, framingActor,
incident->{incidentId, title, description, actor, impact},
evidence[]->{evidenceId, timestamp, type, description, status,
source[]->{sourceId, title, publisher, url}},
events[]->{eventId, description, timestamp, status,
evidence[]->{evidenceId}}
}
This is useful for security analysis because the response remains structured.
The agent can filter on timestamps, statuses, evidence IDs, and relationship
types instead of trying to recover those distinctions from prose.
Queries are parameterized with caseId and incidentRef. The selected case
defines the visible evidence set, and the context derives a set of allowed
evidence IDs before returning events, relationships, or claims. A relationship
is included only when its supporting evidence and both endpoint events are in
scope.
This is how the implementation handles temporal safety. CASE-004 is not just
a label in the UI; its Sanity references define the evidence boundary. A
day-one question can also apply an additional D1 filter before the result is
rendered. The model cannot accidentally cite a later ransomware event as if it
were known on day one.
The Node context uses Sanity's Content API with the project ID, dataset, GROQ
query, and optional server-side token. The token is loaded from .env on the
server and is never exposed to the browser. The browser talks to the local web
server, while the local server talks to Sanity.
That separation gives the project a safer shape:
Browser UI → local investigation API → Sanity Content API
↑
server-side token
The same API boundary also makes failures explicit. A missing case produces a
clear error, while an empty evidence set is handled as an empty investigation
scope rather than causing a client-side length exception.
The project includes a standalone Sanity Studio configured for the same project
and production dataset. The Structure Tool provides the editing workspace
for the seven document types, with previews showing useful forensic identifiers
such as CASE-004, E-021, or an event description.
The Vision Tool is enabled for inspecting and testing GROQ directly in Studio.
That is useful when developing an investigation query: I can verify a case
projection, inspect references, and test a cutoff query against the real
dataset before wiring it into the MCP context.
The importer uses Sanity mutations in batches rather than hand-editing hundreds
of documents. It creates or replaces the normalized documents in dependency
order, then applies reverse-reference patches for evidence-to-event,
evidence-to-claim, and contradiction links.
The import is repeatable and testable:
npm run sanity:validate # validate source inventory
node scripts/sanity-seed.mjs --dry-run
npm run sanity:seed # write the content to Sanity
This gives the benchmark a reproducible migration path while keeping the live
knowledge base editable in Studio. It also means the dataset can be rebuilt if
the schema gains another forensic field later.
Sanity stores and retrieves the content; the MCP server exposes investigation
capabilities to an agent host. The server provides list_investigation_cases
and investigate_case(caseId, question). Internally, those tools call the same
Sanity context used by the web UI.
This division is intentional. Sanity is responsible for content modeling,
relationships, querying, and provenance. The context layer is responsible for
case scoping and graph filtering. The MCP layer is responsible for making those
grounded operations available to an agent. The agent can therefore reason over
retrieved evidence without receiving unrestricted access to the whole dataset.
Sanity stores seven document types:
incident: case narrative, actor, impact, confidence, and source referencessource: publisher, URL, publication date, type, and reliability evidence: an observation with timestamp, status, description, and provenanceevent: a reconstructed action or state with evidence references relationship: a typed link between events claim: an assertion that can be supported or contradicted investigationCase: a benchmark scope with mode, cutoff, and actor framing
The schema is defined with Sanity's typed helpers:
defineField({
name: 'supportsEvents',
type: 'array',
of: [defineArrayMember({type: 'reference', to: [{type: 'event'}]})],
})
defineField({
name: 'relationship',
type: 'string',
options: {list: ['precedes', 'enables', 'causes', 'depends_on']},
})
IDs from the benchmark remain explicit and stable: INC-001, E-001, N01,
and CASE-004. This makes the imported content easy to inspect in Studio and
keeps evidence references understandable in the investigation output.
The JSONL files under data/ remain the reproducible source of truth. The
importer maps them into Sanity documents:
incidents.jsonl → incident
sources.jsonl → source
evidence.jsonl → evidence
attack_graphs.nodes → event + claim
attack_graphs.edges → relationship
benchmark_cases.jsonl → investigationCase
The importer writes documents in dependency order and applies reverse evidence
references in a second pass because events and evidence point to each other.
npm run sanity:validate
node scripts/sanity-seed.mjs --dry-run
npm run sanity:seed
The current Sanity dataset contains 445 imported Cyber Autopsy documents in
production, plus 12 existing documents in the project.
agent/sanity-context.mjs uses scoped GROQ queries. It does not download the
entire dataset for every question:
*[_type == "investigationCase" && caseId == $caseId][0] {
caseId, mode, temporalCutoff, framingActor,
incident->{incidentId, title, actor, actorType},
evidence[]->{evidenceId, timestamp, type, description, status,
source[]->{sourceId, title, publisher, url}},
events[]->{eventId, description, timestamp, status,
evidence[]->{evidenceId}}
}
The context then resolves relationships and claims and filters them against the
case's allowed evidence IDs. That filtering is the temporal safety boundary:
CASE-004 can answer what was knowable at the end of day one without leaking
later ransomware evidence from the full case.
The MCP server exposes two tools:
list_investigation_cases
investigate_case(caseId, question)
The investigation function maps question intent to structured graph operations:
predecessors, enablers, supporting evidence, statuses, cutoff checks, actor
framing, and contradictions. The result is deterministic, grounded in Sanity
content, and designed to be passed to a model without requiring another model
API key in this repository.
Project ID: 41l9o4xn
Dataset: production
Organization ID: oqf9m6vy6
The public Sanity project details are intentionally included so the structured
content model can be inspected. The server-side SANITY_API_TOKEN stays in
.env, is ignored by Git, and is never sent to the browser.
The local agent is available through the MCP server:
npm run agent:mcp
The browser UI uses the same investigation context and exposes the workflow in
a more approachable way:
Useful sessions to capture for the final submission are:
CASE-001: How did the attacker move from initial access to ransomware deployment?
CASE-001: What evidence supports the Rclone event?
CASE-004: What could we have known at the end of day one?
CASE-011/012: What does the evidence establish regardless of actor framing?
CASE-001: Are there conflicting claims?
The current build has been checked with:
.venv/bin/pytest -q
npm run sanity:validate
npm --prefix studio-cyber-autopsy run build -- --no-auto-updates
The browser smoke checks cover the full investigation, the CASE-004 temporal
cutoff, case descriptions, evidence rendering, and zero browser console errors.