5 Blazing Steps to a SEC Item 1.05 Pipeline (Cyber 8-K)

Engineering leaders are now expected to decide fast—and defend later. This guide shows how to ship a developer-friendly SEC Item 1.05 pipeline that automates cyber 8-K automation, materiality assessment, and disclosure evidence collection. You’ll get production-ready code, CI/CD examples, and a signed evidence store pattern—so the decision clock doesn’t beat you.

SEC Item 1.05 Pipeline (Cyber 8-K)

What we’re building

A SEC Item 1.05 pipeline that:

  1. Models “materiality drivers” in code (systems affected, customer impact, operational disruption, $$ loss).
  2. Auto-assembles Item 1.05 fact patterns (nature, scope, timing, impact) from logs, tickets, and PRs.
  3. Tags incidents in CI/CD with severity + probable materiality.
  4. Packages reproducible evidence to a signed, immutable evidence store.
  5. Starts four-business-day timers from determination of materiality, and routes non-material events to Item 8.01.

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Step 1 — Model materiality drivers in code

Below is a simple Python domain model to score probable materiality. Adjust thresholds to match your risk appetite.

# materiality.py
from dataclasses import dataclass
from enum import Enum
from typing import List, Dict

class SystemTier(Enum):
    TIER_1 = 3   # critical (prod auth, payments, PII lake)
    TIER_2 = 2   # important
    TIER_3 = 1   # ancillary

@dataclass
class Impact:
    customers_affected: int              # unique customers/users
    revenue_at_risk_usd: float
    operational_disruption_hours: float  # downtime or severe degradation
    data_confidentiality_loss: bool
    data_integrity_loss: bool
    data_availability_loss: bool

@dataclass
class IncidentContext:
    id: str
    systems: List[SystemTier]
    severity: str                        # Sev1..Sev4
    impact: Impact
    regulators_notified: bool = False

MATERIALITY_WEIGHTS: Dict[str, float] = {
    "tier": 1.25, "customers": 1.0, "revenue": 1.1, "ops": 0.9,
    "c": 0.8, "i": 0.8, "a": 0.8, "sev": 1.2
}

def probable_materiality_score(ctx: IncidentContext) -> float:
    tier_score = sum(t.value for t in ctx.systems) * MATERIALITY_WEIGHTS["tier"]
    cust_score = min(ctx.impact.customers_affected / 1000, 10) * MATERIALITY_WEIGHTS["customers"]
    rev_score  = min(ctx.impact.revenue_at_risk_usd / 1_000_000, 10) * MATERIALITY_WEIGHTS["revenue"]
    ops_score  = min(ctx.impact.operational_disruption_hours / 2, 10) * MATERIALITY_WEIGHTS["ops"]
    cia_score  = (
        (1 if ctx.impact.data_confidentiality_loss else 0) * MATERIALITY_WEIGHTS["c"] +
        (1 if ctx.impact.data_integrity_loss else 0)       * MATERIALITY_WEIGHTS["i"] +
        (1 if ctx.impact.data_availability_loss else 0)    * MATERIALITY_WEIGHTS["a"]
    )
    sev_map = {"Sev1": 4, "Sev2": 2, "Sev3": 1, "Sev4": 0}
    sev_score = sev_map.get(ctx.severity, 0) * MATERIALITY_WEIGHTS["sev"]
    return round(tier_score + cust_score + rev_score + ops_score + cia_score + sev_score, 2)

def is_probably_material(ctx: IncidentContext) -> bool:
    return probable_materiality_score(ctx) >= 10.0  # tune threshold with Legal

Bonus — Attach an external posture snapshot from our free tool

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Step 2 — Auto-assemble Item 1.05 fact patterns

Aggregate nature, scope, timing, impact from observability, ticketing, and PR trails.

// fact_pattern.ts (Node 20+)
import { promises as fs } from "fs";

type Evidence = { type: string; path: string; digest?: string; };
type FactPattern = {
  incidentId: string;
  nature: string;     // what happened
  scope: string;      // what assets/data
  timing: string;     // discovery + determination timestamps
  impact: string;     // customers, $$, operations
  evidence: Evidence[];
};

export async function buildFactPattern(incidentId: string): Promise<FactPattern> {
  const ticket = JSON.parse(await fs.readFile(`./.evidence/${incidentId}/ticket.json`, "utf8"));
  const pr     = JSON.parse(await fs.readFile(`./.evidence/${incidentId}/pr.json`, "utf8"));
  const logs   = await fs.readFile(`./.evidence/${incidentId}/alerts.ndjson`, "utf8");

  return {
    incidentId,
    nature: ticket.summary,
    scope: `Services: ${ticket.services.join(", ")}; Data: ${ticket.data_involved.join(", ")}`,
    timing: `Detected: ${ticket.detected_at}; Determined: ${ticket.materiality_determined_at ?? "TBD"}`,
    impact: `Users: ${ticket.users_impacted}; RevRiskUSD: ${ticket.revenue_risk}; OpsHours: ${ticket.ops_hours}`,
    evidence: [
      { type: "ticket", path: `ticket.json` },
      { type: "pr",     path: `pr.json` },
      { type: "alerts", path: `alerts.ndjson` }
    ]
  };
}

Tip: When your IR lead clicks “Material” in the ticket, stamp materiality_determined_at—that’s the legal start for the four-business-day timer.


Step 3 — CI/CD: tag incidents & package evidence (to a signed store)

A GitHub Actions workflow that routes to Item 1.05 vs Item 8.01, signs artifacts, and publishes to an immutable bucket.

# .github/workflows/incident-evidence.yml
name: Incident Evidence Package

on:
  workflow_dispatch:
    inputs:
      incident_id:
        description: "Incident ID"
        required: true
  push:
    paths:
      - ".evidence/**"

jobs:
  package:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v5
        with: { python-version: "3.11" }

      - name: Compute probable materiality
        run: |
          pip install orjson
          python - <<'PY'
          import json, sys, orjson, pathlib
          from materiality import IncidentContext, Impact, SystemTier, is_probably_material
          iid = "${{ github.event.inputs.incident_id || 'latest' }}"
          ticket = orjson.loads(pathlib.Path(f".evidence/{iid}/ticket.json").read_bytes())
          ctx = IncidentContext(
              id=iid,
              systems=[SystemTier[s] for s in ticket["systems"]],
              severity=ticket["severity"],
              impact=Impact(
                  customers_affected=ticket["users_impacted"],
                  revenue_at_risk_usd=ticket["revenue_risk"],
                  operational_disruption_hours=ticket["ops_hours"],
                  data_confidentiality_loss=ticket["c_loss"],
                  data_integrity_loss=ticket["i_loss"],
                  data_availability_loss=ticket["a_loss"]
              )
          )
          print("probable_material:", "true" if is_probably_material(ctx) else "false")
          PY

      - name: Create reproducible tarball
        run: |
          mkdir -p dist
          tar --sort=name --mtime='UTC 2024-01-01' --owner=0 --group=0 \
              -czf dist/evidence.tar.gz .evidence/${{ github.event.inputs.incident_id }}

      - name: Sign package (Sigstore/cosign or in-toto)
        run: |
          echo "$SIGNING_KEY" | base64 -d > key.pem
          openssl dgst -sha256 -sign key.pem -out dist/evidence.tar.gz.sig dist/evidence.tar.gz
        env:
          SIGNING_KEY: ${{ secrets.SIGNING_KEY_BASE64 }}

      - name: Upload to immutable bucket
        run: |
          aws s3 cp dist/evidence.tar.gz s3://evidence-store/${{ github.event.inputs.incident_id }}/ --sse AES256
          aws s3 cp dist/evidence.tar.gz.sig s3://evidence-store/${{ github.event.inputs.incident_id }}/ --sse AES256

      - name: Decide disclosure path
        id: route
        run: |
          MATERIALITY=$(gh run view ${{ github.run_id }} --log | grep probable_material | tail -1 | awk '{print $2}')
          if [ "$MATERIALITY" = "true" ]; then
            echo "route=item1_05" >> $GITHUB_OUTPUT
          else
            echo "route=item8_01" >> $GITHUB_OUTPUT
          fi

      - name: Post status
        run: echo "Routed to ${{ steps.route.outputs.route }}"
        # next: call your disclosure bot, or open a prefilled doc for Legal

Immutable evidence store (Terraform, AWS S3 with Object Lock + versioning):

# evidence_store.tf
resource "aws_s3_bucket" "evidence" {
  bucket = "evidence-store"
  object_lock_enabled = true
}

resource "aws_s3_bucket_versioning" "evidence" {
  bucket = aws_s3_bucket.evidence.id
  versioning_configuration { status = "Enabled" }
}

resource "aws_s3_bucket_server_side_encryption_configuration" "evidence" {
  bucket = aws_s3_bucket.evidence.id
  rule { apply_server_side_encryption_by_default { sse_algorithm = "AES256" } }
}

resource "aws_s3_bucket_object_lock_configuration" "evidence" {
  bucket = aws_s3_bucket.evidence.id
  rule {
    default_retention {
      mode = "GOVERNANCE" # prevent deletion/shortening without elevate
      days = 365
    }
  }
}

Step 4 — Start the four-business-day timer from determination

The clock starts when you determine materiality, not when you detect the incident. Here’s a simple scheduler that respects business days (no holidays handling shown; plug your calendar later).

# timer.py
from datetime import datetime, timedelta

def add_business_days(start: datetime, days: int) -> datetime:
    d = start
    added = 0
    while added < days:
        d += timedelta(days=1)
        if d.weekday() < 5:  # Mon-Fri
            added += 1
    return d

def item_105_deadline(determined_at_iso: str) -> str:
    start = datetime.fromisoformat(determined_at_iso)
    deadline = add_business_days(start, 4)
    return deadline.isoformat()

print(item_105_deadline("2025-10-23T11:30:00"))

Wire this into your ticket UI so Legal sees a live countdown.


Step 5 — Route Item 1.05 vs Item 8.01 with guardrails

// route.ts
import { buildFactPattern } from "./fact_pattern.js";
import { execSync } from "node:child_process";

export async function routeDisclosure(incidentId: string, material: boolean) {
  const fp = await buildFactPattern(incidentId);
  if (material) {
    // Prepare Item 1.05 disclosure package
    execSync(`gh issue comment ${incidentId} --body "Routed to Item 1.05. Evidence: evidence.tar.gz (signed). Timing: ${fp.timing}"`);
    // trigger 8-K drafting workflow, notify Legal
  } else {
    execSync(`gh issue comment ${incidentId} --body "Routed to Item 8.01 (not material). Evidence retained. Timing: ${fp.timing}"`);
    // optional: prepare 8-K Item 8.01 or internal comms only
  }
}

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Run it here: free.pentesttesting.com (light scan, instant results).


When to call in specialists

If your assessment shows likely materiality—or you need a defensible control map before the next exam—loop in our teams:


Further dev reading on our blog

Keep your engineers sharp with practical posts from the Cyber Rely Blog:


Implementation checklist (copy/paste for your backlog)

  • Add materiality drivers module + tests.
  • Stamp materiality_determined_at in tickets; show Item 1.05 countdown.
  • CI job: build reproducible tar of .evidence/INCIDENT_ID/** and sign.
  • Immutable evidence store with versioning + governance retention.
  • Router: Item 1.05 vs Item 8.01 with Legal notification + templates.
  • Attach external scan result (JSON/PDF) from the free tool.

Final CTAs


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