The Night the SOC Missed It: Real‑Time Detections, Guardrails, and Audit‑Ready Proofs Without Slowing Delivery

The 2 a.m. page that shouldn’t have happened

Two summers ago, I watched a consumer fintech’s SOC miss an interactive shell spawned in a prod container. Cloud logs were flowing, metrics looked healthy, and dashboards were very green. The attacker lived for 52 minutes—long enough to scrape env vars and hit an internal API. The postmortem wasn’t about heroics; it was about gaps. No admission guardrails. No runtime detections. No automated proofs. And worst, the SIEM was full of PII because “we needed context.” Sound familiar?

Here’s what we’ve seen actually work across fintech, healthtech, and adtech: translate policies into guardrails that run at CI and admission, wire real‑time detections that trigger in minutes, and produce automated proofs auditors accept—without slowing delivery or leaking PII.

Translate policy into guardrails that actually run

Policies that live in a wiki don’t block anything. Put them in code and make them binary: pass/fail.

• Use OPA Gatekeeper or Kyverno for Kubernetes admission controls
• Use Conftest to fail CI on Terraform/K8s/Helm misconfig
• For Terraform Cloud/Enterprise, consider Sentinel if you’re already invested, but we prefer OPA for portability

Example: block privileged pods, :latest tags, and force runAsNonRoot.

package kubernetes.admission

deny[msg] {
  input.kind.kind == "Pod"
  some c
  container := input.review.object.spec.containers[c]
  container.securityContext.privileged == true
  msg := sprintf("privileged container %s is not allowed", [container.name])
}

deny[msg] {
  input.kind.kind == "Pod"
  some c
  container := input.review.object.spec.containers[c]
  endswith(container.image, ":latest")
  msg := sprintf("container %s uses :latest tag", [container.name])
}

deny[msg] {
  input.kind.kind == "Pod"
  not input.review.object.spec.securityContext.runAsNonRoot
  msg := "runAsNonRoot must be set at pod or container level"
}

Gatekeeper constraint (trimmed):

apiVersion: constraints.gatekeeper.sh/v1beta1
kind: K8sAllowed
metadata:
  name: gp-no-privileged-no-latest
spec:
  match:
    kinds:
      - apiGroups: [""]
        kinds: ["Pod"]

CI guard with conftest:

# Fail PR if k8s/ or terraform/ violates policies
conftest test k8s/ -p policy/
conftest test terraform/ -p policy/

Pro tip: keep the same policy library for CI and admission. Drift between “what we test” and “what we allow” is how exceptions creep in.

Wire up real-time detections without drowning

You don’t need a seven-figure SIEM to get fast MTTD. You need the right signals and sane routing.

• Runtime: Falco (syscall) or Cilium Tetragon (eBPF) for process/network anomalies
• Control plane: Kubernetes Audit Logs, OPA/Gatekeeper decision logs
• Cloud: AWS CloudTrail + GuardDuty, GCP Audit Logs + Security Command Center, Azure Activity Logs + Defender for Cloud
• IdP: Okta sign‑ins and admin events

Falco rule: alert on bash spawned inside containers and kubeconfig reads.

# falco_rules_local.yaml
- rule: Terminal shell in container
  desc: Detect bash/sh spawned in a container
  condition: spawned_process and container and proc.name in (bash, sh)
  output: "Terminal shell spawned (user=%user.name container=%container.name cmd=%proc.cmdline)"
  priority: WARNING

- rule: Read kubeconfig in container
  desc: Detect reads of kubeconfig
  condition: (open_read) and fd.name startswith "/root/.kube/"
  output: "Kubeconfig read in container (user=%user.name container=%container.name file=%fd.name)"
  priority: WARNING

Ship detections to your router (Kafka/HTTP) with Fluent Bit.

# fluent-bit.conf (snippet)
[INPUT]
  Name              kmsg
  Tag               falco.*

[INPUT]
  Name              tcp
  Listen            0.0.0.0
  Port              2801
  Tag               falco.json

[FILTER]
  Name              throttle
  Match             falco.*
  Rate              1000

[OUTPUT]
  Name              es
  Match             falco.*
  Host              elasticsearch
  Port              9200
  Index             falco-%Y.%m.%d

Route provider findings directly too; don’t re‑implement GuardDuty. Dedup at the edge and tag with env, service, owner to keep false positives manageable.

Automated proofs: evidence auditors accept without screenshots

You can ship fast and still have receipts. Make the pipeline produce cryptographic attestations, store decision logs, and keep immutable artifacts.

• Sign images with Sigstore cosign and publish SLSA provenance
• Emit OPA/Kyverno decision logs to an append‑only bucket
• Record deploys (ArgoCD/Flux) and link commit SHAs to releases

GitHub Actions example that runs policy checks, signs the image, and publishes provenance:

name: build-and-prove
on:
  push:
    branches: [ main ]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Policy check (Conftest)
        run: |
          conftest test k8s/ -p policy/
          conftest test terraform/ -p policy/
      - name: Build image
        run: |
          docker build -t ghcr.io/acme/payments:${{ github.sha }} .
          echo "IMAGE=ghcr.io/acme/payments:${{ github.sha }}" >> $GITHUB_ENV
      - name: Login to GHCR
        run: echo ${{ secrets.GITHUB_TOKEN }} | docker login ghcr.io -u $ --password-stdin
      - name: Push
        run: docker push $IMAGE
      - name: Cosign sign
        env:
          COSIGN_EXPERIMENTAL: "true"
        run: cosign sign --key ${{ secrets.COSIGN_KEY }} $IMAGE
      - name: SLSA provenance
        uses: slsa-framework/slsa-github-generator/actions/generator@v2
        with:
          base64-subjects: ${{ env.IMAGE }}

Evidence you keep:

• Signed image digest, SLSA provenance blob
• Conftest pass report (artifact), Gatekeeper decision logs (bucket)
• ArgoCD deploy event linking commit -> environment

This is the “automated proof” you hand to auditors instead of a screen‑recording marathon.

Regulated data vs speed: keep PII out, keep signals rich

If you’re piping raw PII into your SIEM to “debug incidents faster,” you’re building your own breach headline. Tokenize at the edge and keep context with reversible or keyed hashes.

• Define a policy: PII never leaves prod VPC untransformed
• Tokenize emails/phones in logs at collection time
• Use Postgres RLS and Snowflake masking policies to scope who can read raw data

Hash PII in Fluent Bit using a Lua filter with a secret salt (from Vault):

-- pii_hash.lua
local openssl = require('openssl')
function hash(s, salt)
  return openssl.digest.new('sha256'):final(s .. salt)
end

function cb(tag, timestamp, record)
  local salt = os.getenv('PII_SALT') or ''
  if record['user_email'] then
    record['user_email_hash'] = hash(record['user_email'], salt)
    record['user_email'] = nil
  end
  return 1, timestamp, record
end

Wire it in Fluent Bit:

[FILTER]
  Name    lua
  Match   app.*
  script  /fluent-bit/scripts/pii_hash.lua
  call    cb

Database protections that won’t slow you down:

• Postgres RLS: enforce tenant/user scoping at the DB layer
• Snowflake: MASKING POLICY for columns like email, ssn
• mTLS with a mesh (Istio/Cilium) and egress policies to stop data drip

You still correlate using the hash, you just don’t leak PII into every log store and dashboard.

Ship detections like code: test, canary, promote, rollback

Most teams toss rules into the SIEM and hope. Treat detections like features.

1. Write detection with tests (sample events) in Git
2. Run unit tests in CI; spin a sandbox to replay prod‑like data
3. Canary to 10% of namespaces or one cluster
4. Measure precision/recall for a week; gate on false positive SLO
5. Promote with a version tag; rollback by revert

Example test for a Falco rule using replay:

falco --rules falco_rules_local.yaml --trace-file sample_syscalls.scap --disable-source k8s_audit

Honeytokens are cheap wins: drop a Canarytoken API key in your private repo; any use should page immediately. We’ve caught red‑teamers and one unlucky contractor this way.

Metrics that matter and a 60‑day plan

You can’t improve what you don’t measure.

• MTTD (p95): target < 5 minutes for critical events
• MTTR (p95): target < 30 minutes with runbooks
• False positive rate: < 5% per rule over 7 days
• Coverage: % workloads under guardrails, % clusters with runtime sensors
• Evidence freshness: time from deploy to attestation available (< 5 minutes)

A pragmatic 60‑day rollout:

• Days 1–10: pick top 10 guardrails; implement OPA/Kyverno; add Conftest to CI
• Days 11–20: deploy Falco/Tetragon; route to SIEM; integrate GuardDuty/SCC
• Days 21–30: add cosign + SLSA; store OPA decision logs; wire ArgoCD evidence
• Days 31–45: tokenize PII at the edge; enable RLS/masking; backfill docs
• Days 46–60: build rule testing/canary pipeline; set SLOs; quarterly review cadence

At a payments client, this cut p95 MTTD from 47 min to 3 min and eliminated PII in their SIEM in three sprints. Zero slowdown in deploy frequency (still ~40/day).

What GitPlumbers does on these engagements

We’ve done this at seed‑stage startups and at public fintechs. The pattern works.

• Rapid policy pack: OPA/Kyverno guardrails aligned to your stack
• Runtime detections: Falco or Tetragon, tuned to your risk model
• Evidence plumbing: cosign/SLSA, decision logs, ArgoCD hooks
• Data hygiene: edge tokenization, RLS/masking, mesh egress
• Detection SLOs and a rule lifecycle that ops will actually maintain

If you want a partner who’s burned their hands on this stuff and still ships, we’ll help you wire it in without blowing up your roadmap.