01 · Questionnaire

Questionnaire

Answer per control. Each answer feeds the evaluation and the traceability chain Actor → Vector → Component → Effect → Audit.

C-3.1 · L3
High

SBOM completeness

Is a complete SBOM available for every shipped software & firmware artefact?

Evidence required:CycloneDX / SPDX SBOMs per release.
Why it matters ▾

A Software Bill of Materials lists every open-source and third-party component (and version) inside a build. Without it you cannot answer 'are we affected by CVE-X?' in hours. SBOMs must cover firmware and embedded artefacts, not just server software.

C-3.2 · L3
Critical

Signed update chain

Are all firmware and software updates signed, with on-device signature verification?

Evidence required:Code-signing policy, secure boot logs.
Why it matters ▾

Every firmware/software update must be cryptographically signed by a controlled key, and the device must verify the signature before accepting it. This blocks the classic 'malicious update' supply chain attack — the highest-impact path into deployed equipment.

C-3.3 · L3
High

FPGA bitstream provenance

Is bitstream origin verified and locked against unauthorised reconfiguration?

Evidence required:Bitstream signature, lockbit settings, vendor attestation.
Why it matters ▾

FPGAs are reconfigured from a bitstream that defines their hardware behavior. Unverified bitstreams can implement hidden backdoors invisible to software analysis. Origin verification, encrypted bitstreams and lockbit settings make unauthorized reconfiguration practically impossible.

C-3.4 · L3
Medium

Toolchain integrity

Is the build toolchain reproducible and integrity-verified across releases?

Evidence required:Reproducible build report, hash log.
Why it matters ▾

If the build toolchain (compiler, linker, signing tools) is tampered with, every binary it produces is suspect — the classic Ken Thompson attack. Reproducible builds let independent parties verify that source code maps deterministically to the released binary.

C-3.5 · L3
High

AI/ML model provenance

Are ATR / autonomy models tracked with training data lineage and version control?

Evidence required:Model card, dataset hash, training pipeline log.
Why it matters ▾

AI/ML models (automatic target recognition, autonomy) inherit risks from their training data and pipeline. Tracking model versions, dataset hashes and training environment lets you reproduce a model, investigate misbehavior and respond to data-poisoning disclosures.

C-3.6 · L3
High

Tier-2/3 sub-supplier disclosure

Are Tier-2 and Tier-3 sub-suppliers disclosed for safety- and mission-critical components?

Evidence required:Supplier tree, country-of-origin matrix.
Why it matters ▾

Risk is rarely at the integrator level — it is in the chip foundry, the sensor crystal supplier, the open-source library two layers deep. Disclosure of Tier-2/3 sub-suppliers for safety- and mission-critical parts lets the buyer assess country-of-origin and concentration risk.

C-3.7 · L3
High

Vulnerability management & re-validation

Is there a defined SLA for CVE triage, patching and incident-triggered re-validation of firmware?

Evidence required:VM policy, patch SLA, re-validation records.
Why it matters ▾

New vulnerabilities are disclosed daily. A defined SLA for triage (is this CVE applicable?), patching, and re-validation (does the patched firmware still meet safety requirements?) prevents both 'unpatched for years' and 'rushed patch breaks the platform' failure modes.

C-3.8 · L3
High

Build & engineering toolchain isolation

Are build servers, signing infrastructure and engineering workstations isolated from general IT and internet?

Evidence required:Network diagram, access policy, audit log.
Why it matters ▾

Build infrastructure and signing keys are the crown jewels. If a build server is on the corporate intranet with email access, a phishing email can compromise every product the company ships. Isolation, MFA, and audit logging are non-negotiable.

C-3.9 · L3
High

AI/ML adversarial robustness

Are ATR / autonomy models tested against adversarial inputs, sensor evasion and data poisoning?

Evidence required:Adversarial test suite, red-team report.
Why it matters ▾

ATR and autonomy models can be fooled by inputs crafted to evade detection (adversarial patches, acoustic shapes) or by long-term data-poisoning of public training sets. Red-team testing against these attack classes must happen before deployment, not after an incident.

C-3.10 · L3
Medium

Payload interface trust

Are third-party payload interfaces (data + power) gated by an authenticated, schema-validated bridge?

Evidence required:Interface spec, schema validator, gateway logs.
Why it matters ▾

Third-party payloads (additional sensors, communication modules) plug into the platform. Without an authenticated, schema-validated bridge, a malicious or buggy payload can inject commands, exfiltrate data, or cause faults in the host platform.