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SecOpsAI Research Automation

SecOpsAI Research turns a watchlist lead into a defensible investigation without executing the investigated package. The dashboard buttons call the same typed Core workflow as the CLI; command-copy helpers are only a fallback.

Primary Mission Control workflow

The normal workflow no longer requires an evidence-bundle export, a file upload, or a copied prompt.

  1. Open Research, select a case, and confirm that it has an active package or extension subject.
  2. When you know the verified legitimate package, enter it under Legitimate comparison package. Leave it empty when ownership is not yet verified; SecOpsAI will not guess.
  3. Click Run Investigation Pipeline.
  4. Core collects official-registry metadata and the package artifact using bounded static-intake controls. It records hashes, inspects the archive without execution, and performs a deterministic comparison when a trusted reference was supplied.
  5. Core queues three durable Intelligence jobs. The installed Local Codex Bridge reads only minimized case and static-analysis context and writes structured proposals back to the same pipeline record.
  6. Mission Control refreshes the running pipeline automatically.
  7. In agent review mode, SecOpsAI accepts the bounded evidence proposals, records an evidence-linked agent verdict, and reruns publication safety automatically. The verdict is constrained by deterministic guardrails: local absence cannot prove benignness, credible requires advisory-backed or sandbox evidence, and low-confidence work remains inconclusive.
  8. In supervised mode, review each proposal manually with Accept or Reject, then record the verdict yourself.
  9. Review the resulting case. External sandbox submission, external disclosure delivery, and final publication approval remain separate human gates.

If the bridge or collection step fails, click Retry from checkpoint. A new pipeline revision is created, stale proposals are superseded, and the previous revision remains auditable. If comparison was incomplete, enter a verified reference and click Add reference and rerun analysis.

If an exact package version is later removed from its registry, the pipeline may reuse a previously collected local quarantine artifact only when its ecosystem, package, version, byte size, and SHA-256 all match. The reuse reason and failed registry retrieval are recorded in the step result. SecOpsAI never substitutes another version or trusts an unverified local file.

The pipeline never executes package code, submits an artifact to an external sandbox, sends external communication, approves publication, or publishes an article. In agent-review mode it may record a bounded case verdict, but only against accepted, pipeline-specific evidence and with all guardrail decisions retained in the audit trail.

Agent-review mode

Agent-review mode is the recommended high-automation setting for a local research workstation. It delegates the repeatable evidence review, verdict recommendation, analyst brief, and publication preflight to the selected Local Codex/OpenCodex model. Models remain fallible; SecOpsAI therefore validates their schema, evidence links, confidence, contradictions, and local-exposure reasoning before writing a verdict.

Install or update the background bridge in agent-review mode:

secopsai intelligence bridge service install \
  --autonomy-mode agent_review \
  --model kimi/kimi-k2.7-code-highspeed

Complete an already-waiting pipeline from the CLI:

secopsai research pipeline agent-complete RPL-XXXXXXXXXXXXXXXX

Mission Control exposes the same operation as Complete Agent Review. The action:

  • accepts bounded static evidence and model proposals;
  • records a verdict with confidence, rationale, evidence identifiers, model provider, and pipeline revision;
  • blocks benign or not-substantiated conclusions based only on missing local exposure;
  • downgrades unsupported or contradictory claims;
  • reruns publication safety;
  • does not upload an artifact, contact a third party, or publish content.

The model identifier is stored in the user service definition, not only in the browser session. Provider credentials remain owned by OpenCodex or Codex and are never copied into the service file.

Use supervised mode when policy requires proposal-by-proposal human acceptance:

secopsai intelligence bridge service install --autonomy-mode supervised

Granular recovery controls

Use the individual actions only when diagnosing a step or deliberately running a narrower workflow:

  1. Create or open a Research Case from Supply Chain Triage.
  2. Select the ecosystem, package, and optional version.
  3. Use Collect Metadata Preview to confirm the official registry target.
  4. Use Run Safe Package Intake. The artifact is fetched into local quarantine, hashed, and inspected in memory. No package manager or build command runs.
  5. Review the job result and indicators. Use Attach Verified Evidence only after reviewing the preview.
  6. Use Generate Evidence Matrix and either click Complete Agent Review or record a human verdict with rationale and evidence IDs.
  7. Use Run Publication Safety Check before drafting public content.
  8. Use Prepare Disclosure to create a reviewable maintainer/registry message. Approval and sending are separate actions.
  9. Use Request Sandbox Approval only when static evidence leaves an important runtime question. The default provider is manual-result-import; Core never executes packages locally.
  10. Approve the publication review, create the Blog Ops draft, edit it, and complete the existing Blog Ops approval/publish workflow.

Supported intake ecosystems

The common adapter contract supports npm, PyPI, NuGet, Maven Central, RubyGems, Packagist, Go modules, and Open VSX. CI uses fixtures, not live registry calls. Adapters enforce their own official metadata and artifact host allowlists.

CLI fallback

secopsai research intake preview --ecosystem npm --package chalk-tempalte
secopsai research intake run --case RSC-XXXXXXXXXXXX --ecosystem npm --package chalk-tempalte
secopsai research jobs list --case RSC-XXXXXXXXXXXX
secopsai research intake attach JOB-XXXXXXXXXXXX
secopsai research workflow evidence-matrix RSC-XXXXXXXXXXXX
secopsai research workflow publication-check RSC-XXXXXXXXXXXX

Human-gated external actions are explicit:

secopsai research workflow verdict RSC-XXXXXXXXXXXX --verdict likely --confidence 70 --rationale "Explain the evidence and limitations." --evidence-id EVD-XXXXXXXXXXXX
secopsai research workflow prepare-disclosure RSC-XXXXXXXXXXXX --recipient security@example.org
secopsai research workflow request-sandbox RSC-XXXXXXXXXXXX --artifact-sha256 <sha256> --justification "Explain the unanswered runtime question."
secopsai research workflow publication-approve RSC-XXXXXXXXXXXX --review-id PUB-XXXXXXXXXXXX

Security boundary

Artifacts are bounded by size, redirect, archive-entry, expanded-content, and inspected-text limits. Absolute paths, traversal, links, devices, archive bombs, private-address resolutions, credential-bearing URLs, and unapproved redirects are rejected. Static indicators are evidence leads, not proof of maliciousness. Raw artifacts and raw registry responses are not sent to AI.

Dynamic analysis is not enabled by pretending the Core host is a sandbox. Configure a dedicated isolated provider or import a sanitized result manually. External sandbox submission, disclosure sending, and publication approval remain auditable human decisions. A model may prepare the justification or draft, but it cannot perform the external action.