Description

A focused course, tailored for you

Zero Trust Evidence for Hyperscale Security Engineers

Translate service-mesh auth, internal IAM, and ML inference controls into the auditor-ready evidence pack enterprise procurement and regulators ask for.

Your platform has the primitives. The auditor wants the evidence pack. The gap between the two is what blocks B2B deals and stretches every external audit.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Security engineering at a hyperscale platform builds genuine zero-trust primitives: mTLS everywhere, workload attestation, internal IAM with short-lived credentials, service mesh enforcement, model registries, inference gateways with logging. The control quality is high. The control evidence, written in a form an external auditor or enterprise procurement team accepts, is missing. Partner security questionnaires arrive citing NIST SP 800-207, ISO 27001 Annex A.5 and A.8, SOC 2 CC6, the EU AI Act for any ML inference path classified high-risk, and increasingly the NIS2 essential entity attestation. The engineer who owns the technical answer becomes the bottleneck for a deal worth more than their salary, because the control narrative, the log-query evidence, and the exception register do not exist in shippable form. This course closes that gap. It is the bridge from internal-clean to externally-evidenced.

What you walk away with

Produce a NIST SP 800-207 control narrative mapped to your actual service mesh and IAM stack, ready to ship to enterprise procurement.
Build a service-to-service auth evidence pack with named log sources, audit queries, and config exports for mTLS, workload attestation, and short-lived credentials.
Map the ML inference path to EU AI Act Articles 12 through 15 and produce the model registry, input/output logging, and human oversight evidence required for high-risk classification.
Stand up the segmentation exception register that auditors actually read, with workload-by-workload status and remediation cadence.
Translate internal control quality into external assurance language that closes B2B deals and shortens the audit cycle.

The 12 modules

Module 1. The evidence gap at hyperscale platforms

Why security engineering at a consumer-scale platform produces strong internal controls and weak external evidence. The structural reason the platform team owns the technical answer but not the audit-ready artefact. The four artefacts every enterprise procurement and external auditor asks for, named explicitly, and where most platforms fall short on each. The specific shape of what a shippable evidence pack looks like, with one fully worked example from a peer hyperscaler.

Module 2. NIST SP 800-207 mapped to your service mesh

Tenet by tenet walk through 800-207 applied to a real service-mesh-and-attestation stack. How the seven core tenets translate to specific service mesh policy primitives, SPIFFE/SPIRE workload identities, and internal IAM short-lived credentials. The control narrative template auditors accept. The exact phrasing that turns a mesh policy into a 207 control statement. The mistake of mapping at the architectural-diagram level instead of the workload-by-workload level.

Module 3. Service-to-service auth evidence pack

The actual artefact: mTLS root chain documentation, workload attestation evidence (SPIFFE SVID issuance audit logs, attestation policy exports), short-lived credential rotation evidence, the audit query that proves a given workload's identity was attested at a given time. Downloadable log-query templates for common platforms (BoringSSL-based meshes, Envoy, Istio, internal-fork stacks). The procurement-side reader and what they look for first.

Module 4. Internal IAM as an external assurance artefact

Mapping the internal IAM model (short-lived creds, role-based access, attribute-based access, break-glass paths) to ISO 27001 Annex A.5 and A.8 and SOC 2 CC6.1 through CC6.8. The break-glass log-query template. The privileged-access review evidence pack. The risk-acceptance language for the exceptions that always exist (legacy admin paths, vendor support access, debugger access for SREs).

Module 5. Service mesh policy as a control surface

How service mesh policies (authorization policies, peer authentication policies, mTLS modes) become the actual control surface auditors evidence against. The evidence template that shows a policy was in force, was enforced, and was monitored for drift. The drift detection log query. The mistake of evidencing policy intent without evidencing policy enforcement. Worked example: a peer-authentication policy on the inference path, with the exact evidence pack.

Module 6. ML inference governance and EU AI Act Article 12-15

The ML inference gateway as a regulated control surface. EU AI Act high-risk classification triggers, Article 12 logging requirements, Article 13 transparency and information provision, Article 14 human oversight, Article 15 accuracy and robustness. The model registry as the central evidence object. Input/output logging templates that satisfy Article 12 without violating data minimisation. The human oversight evidence pack for the specific workflows that need it.

Module 7. Workload attestation as supply-chain evidence

Translating workload attestation into supply-chain provenance evidence. The SLSA framework mapped to your build and deploy pipeline. The artefact: a signed attestation chain from source to running workload, with the audit log query that proves it. Reproducible-build evidence for the workloads where it matters. The exception register for the workloads where it does not, with risk-accepted rationale.

Module 8. Segmentation exception register

The single artefact auditors read first and that determines audit length. The exception register lists every workload not yet meeting full ZT segmentation, the compensating control, the remediation owner, and the target cadence. The template covers both north-south and east-west segmentation exceptions. The cadence that makes auditors stop asking follow-up questions, with the actual review-meeting agenda that produces evidence of cadence.

Module 9. Logging and detection as control evidence

How platform telemetry becomes audit-ready control evidence. The five log sources every external assurance reader asks for: identity events, policy enforcement events, attestation events, privileged access events, anomaly detection events. The retention policy that satisfies both ISO 27001 A.8.15 and EU AI Act Article 12. The query template for the four common control-effectiveness assertions auditors test.

Module 10. Partner security questionnaire response pack

The artefact that closes deals. A response template covering the 60 most common questions in enterprise procurement security questionnaires, with placeholder evidence references that point at the specific artefacts produced in modules 2 through 9. The 12 questions that are deal-breakers if answered wrong. The legal review handoff that does not slow the response cycle. Worked example: a financial-services partner questionnaire, fully answered.

Module 11. External audit readiness for SOC 2 and ISO 27001

The annual external audit cycle from the platform security engineer's perspective. The walkthrough meeting agenda that minimises engineering time. The evidence index that the audit team uses to self-serve. The sampling protocol that reduces re-pulls. The handful of control families where platform security engineering always becomes the bottleneck, and the prep work that eliminates that bottleneck. Exit criteria for the audit cycle.

Module 12. Ship the evidence pack to the business

How to package the four artefacts (control narrative, service-to-service evidence pack, ML inference governance pack, segmentation exception register) for the three internal audiences that consume it: enterprise sales for procurement responses, legal for regulator inquiries, internal audit for the annual cycle. The publication cadence. The version control approach. The handoff to GRC that does not lose technical fidelity. The escalation path when an audit finding lands and the platform team owns the remediation.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 to 3 cover the gap diagnosis and the foundational artefacts every evidence pack rests on.

Modules 4 to 7 build the four substantive evidence packs: IAM, service mesh policy, ML inference governance, supply-chain attestation.

Modules 8 and 9 cover the cross-cutting artefacts: the exception register and the telemetry control evidence layer that both procurement and auditors test against.

Modules 10 to 12 cover externalisation: the partner questionnaire response pack, the external audit cycle, and the handoff to the internal audiences that consume the evidence.

What you get with this course

Twelve text-based modules in the Art of Service learning environment, each with downloadable templates and worked examples.
Control narrative templates for NIST SP 800-207, ISO 27001 Annex A.5 and A.8, SOC 2 CC6, EU AI Act Article 12 to 15.
Audit-log query templates for common service mesh, attestation, and IAM platforms.
Partner security questionnaire response pack template covering the 60 most common questions.
Hand-built implementation playbook tailored to your platform stack, delivered alongside course access.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours: course access provisioned and the hand-built implementation playbook delivered alongside it.

Week 1: complete modules 1 to 3, produce a first-draft 800-207 control narrative for your stack.

Week 2 to 3: complete modules 4 to 7, produce the four substantive evidence packs.

Week 4: complete modules 8 to 12, ship the partner questionnaire response pack and the audit readiness index.

Before and after

Before

Internal controls are strong. External evidence is patchy. Partner security questionnaires take three weeks. The annual audit drags. The ML inference path has no regulator-ready governance artefact. Every external assurance request becomes an engineering tax.

After

Four shippable evidence packs cover the platform. Procurement questionnaires turn around in days. The audit cycle shortens. The ML inference path has a defensible high-risk governance pack. External assurance is a publication cadence rather than a recurring engineering scramble.

What happens if you do not address this

Enterprise B2B deals stall in procurement because the evidence pack is not shippable. External audits over-run because evidence is reconstructed each cycle. An EU AI Act inquiry on the inference path lands and the high-risk governance artefacts do not exist. The engineer who owns the technical answer keeps becoming the bottleneck for revenue and for regulatory response.

Who it is for

A software or security engineer working on platform security, identity, service mesh, ML inference gateways, or attestation services at a hyperscale consumer or social platform. Strong on internal primitives. Asked increasingly to produce evidence packs for enterprise B2B customers, regulator inquiries, or annual external audits. The role title varies (security engineer, infra security, ML platform security). The pattern is the same: the controls are real, the externally-readable evidence is patchy.

Who this is NOT for. GRC analysts who write policy and do not touch the platform. External auditors. Anyone whose job is procurement of security software rather than building it. The course assumes the controls already exist in the platform and need to be evidenced, not designed from scratch.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Six to ten hours total reading and template work, plus the time needed to instantiate the evidence packs against your specific stack. Self-paced.

Why $199 is the right number

Free NIST and ENISA publications give the framework text. Big4 advisory engagements produce a control narrative for a fee an order of magnitude higher and stop short of the audit-log query templates and the questionnaire response pack. Internal GRC writes policy, not platform-specific evidence. This course sits in the gap: the bridge from platform-engineering reality to externally-readable assurance.

FAQ

Does this assume a specific service mesh?

No. Templates cover Envoy-family meshes (Istio, internal forks), SPIFFE/SPIRE-based attestation, and the common Boring-family TLS stacks. The implementation playbook is hand-built to your specific stack on delivery.

Is the EU AI Act module relevant if our high-risk inference paths are not in EU jurisdiction?

The Article 12 to 15 evidence patterns translate directly to other emerging AI governance frameworks (UK pro-innovation guidance, US sector-specific rules, Singapore Model AI Governance). The module covers the EU shape because it is the most prescriptive; the artefacts transfer.

Does this replace our internal GRC team?

No. It produces the artefacts platform security engineering owns and hands to GRC. GRC remains the policy and audit-management function. The course removes the bottleneck where GRC asks platform engineering for evidence that does not yet exist.

What format are the templates?

Markdown control narratives, CSV exception registers, query templates in vendor-neutral and vendor-specific forms, and a structured response template for the partner questionnaire pack.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.