Formal-Methods–Integrated CI/CD for Safety-Critical AI

Formal-Methods–Integrated CI/CD for Safety-Critical AI

Voruganti Kiran Kumar
Senior DevOps Architect

Abstract— Software for cyber-physical and AI-enabled systems increasingly ships through fast continuous integration and continuous delivery (CI/CD), yet safety, security, and governance properties are rarely checked with formal rigor before release. This paper proposes a practical architecture that integrates lightweight formal methods—machine-checkable specifications and model checking—into code review, pipeline execution, and cluster admission. We compile a small set of high-value invariants (data-flow constraints, concurrency and configuration safety, and cryptography/provenance requirements) drawn from established standards and guidance into machine-verifiable checks. These are enforced at pre-merge and pre-admission, and the results (proof summaries or counterexamples) are packaged as signed attestations that travel with the build. The approach leverages production-proven primitives—policy-as-code engines, Kubernetes admission control, provenance and bill-of-materials standards, and progressive delivery with rollback—to prevent non-conformant changes from reaching users while maintaining delivery velocity. We describe specification templates, policy compilation, an evidence model for auditor replay, and an evaluation protocol focused on violation prevention, admission latency, and auditability. The design is grounded in widely cited industrial experience with lightweight formal methods and current DevSecOps and AI-governance standards.

Index Terms— CI/CD, formal verification, TLA+, Alloy, policy-as-code, Kubernetes admission control, SRE, supply-chain security, SLSA, SBOM, AI governance.

I. Introduction

Delivery automation has matured to the point where most organizations can build, test, and ship multiple times per day. In safety-critical and AI-enabled contexts, however, the questions that matter most—“Is the change safe under concurrency?”, “Does it violate data-handling obligations?”, “Is the artifact provenance trustworthy?”, “Can we explain the gate decision to auditors?”—are often answered by human review or retrospective audit. That timing is wrong: the bar for trust should be met before exposure, and the evidence should accompany the release.

This paper describes how to embed lightweight formal methods into the delivery control plane so that unsafe or unverifiable changes simply cannot advance. We focus on small, tractable specifications and model-checking runs that target high-leverage properties (e.g., absence of forbidden data flows across namespaces, deadlock-freedom in a simplified service interaction, use of approved cryptography), and we connect the results to policy-as-code enforcement and progressive rollout controls. The objective is not exhaustive verification but decision-time assurance with clear recourse (minimal edits to flip a denial) and auditor-ready evidence.

The contributions are as follows: (1) a reference architecture for integrating formal specifications, model checking, and policy-as-code in CI/CD; (2) a set of reusable specification templates and mapping patterns to cluster and pipeline policies; (3) an evidence model that packages proofs and counterexamples as signed attestations; and (4) an evaluation protocol that measures governance efficacy and latency while maintaining SRE delivery goals.

AI generated image using Imagen by Google Gemini

II. Background and Related Work

Formal methods in production. Large-scale industrial deployments have shown that lightweight formal methods—especially specification languages such as TLA+ and automated model checking—can reveal subtle design flaws that elude testing and review, while remaining practical when focused on small models and high-value questions.

DevSecOps and supply-chain guidance. NIST SP 800-218 (Secure Software Development Framework, SSDF) consolidates secure development practices into actionable tasks (e.g., build integrity, review, vulnerability management). NIST SP 800-204D gives concrete recommendations for integrating software supply-chain security in DevSecOps CI/CD pipelines, emphasizing provenance, attestation, and dependency control. The SLSA v1.0 specification provides levels and tracks for provenance assurance; SPDX and CycloneDX standardize software bills of materials; and projects such as Sigstore and in-toto make artifact signing and attestations practical.

AI governance. The NIST AI Risk Management Framework (AI RMF 1.0) articulates risk-based expectations for trustworthy AI (valid and reliable, safe, secure and resilient, accountable and transparent, explainable, privacy-enhanced, and fair). ISO/IEC 42001:2023 specifies an AI management system (AIMS) with organizational roles and controls. These expectations translate naturally into delivery-time gates and artifacts.

Policy-as-code and cluster enforcement. Open Policy Agent (OPA) with the Rego language is a widely adopted, general-purpose policy engine. In Kubernetes, ValidatingAdmissionPolicy (based on the CEL expression language) provides a low-latency, in-process path for enforcing declarative constraints at resource admission. Progressive delivery and rollback discipline are covered extensively in the SRE literature and practice and are crucial for safe exposure of changes.

III. Problem Formulation

We model a delivery system as a transition system ⟨S,A,T⟩\langle S, A, T \rangle⟨S,A,T⟩ with states SSS (repository, artifacts, environment), actions AAA (edits, builds, deployments), and transition relation TTT. A set of invariants I={I1,…,Ik}\mathcal{I} = \{I_1,\dots, I_k\}I={I1​,…,Ik​} encodes obligations at two levels:

1. Specification-level invariants over an abstract model (e.g., no forbidden data path exists; no deadlock is reachable; all protected connections use approved cryptography).
   
   
   

2. Policy-level constraints at admission and promotion (e.g., pod security baselines; image signature and provenance verification; SBOM completeness).
   
   
   

A change Δ\DeltaΔ is admissible if (a) the abstract model updated by Δ\DeltaΔ satisfies all relevant IiI_iIi​ (verified by model checking) and (b) the concrete manifests and artifacts meet all policy constraints at admission and promotion time. Violations yield counterexamples or deny reasons that are attached to the build and used for recourse.

IV. Approach

A. Specification Templates

We propose small, composable templates that target high-leverage risks:

1. Data-flow safety (privacy/egress). Model namespaces, services, and data classifications. Assert that flows involving sensitive labels (e.g., PII) cannot cross designated boundaries without an approved transformation step (tokenization or anonymization). The specification captures only topology and label propagation, keeping the state space tractable.
   
   
   

2. Concurrency and configuration safety. Model a simplified interaction (e.g., request/lock/retry) between services and controllers to establish absence of deadlock or starvation under bounded workloads. The model abstracts the application while preserving the structure of waits and resource limits.
   
   
   

3. Cryptography and identity. Specify that connections involving protected contexts use approved cipher suites and that artifacts destined for protected environments are signed by authorized identities.
   
   
   

4. Provenance and integrity. Specify that a release contains a valid chain of evidence: SLSA-level provenance, cryptographic signatures, and a complete SBOM. The specification tracks artifact relationships and required predicates (present/valid), not the payload internals.
   
   
   

Each template is accompanied by guidance on scoping (which components it covers), modeling assumptions, and typical counterexamples (e.g., a forbidden path via a recently added service or a missing transformation).

B. Model Checking in the Pipeline

Verification runs at two points:

• Nightly and on change. When configuration or classification files change, affected specifications are re-checked. Results are cached by content hash.

• Pre-merge. Pull requests that touch policy, data classification, or deployment descriptors must pass relevant specifications. A failing run blocks the merge and posts a counterexample trace in the PR for rapid fix and re-check.

Proof results and counterexamples are summarized and packaged as build artifacts, to be signed later as attestations.

C. Policy Compilation and Admission Control

Each invariant maps to executable checks:

• Kubernetes ValidatingAdmissionPolicy (CEL) for low-latency constraints, such as “deny privileged pods in namespaces with compliance label X,” “enforce image registry allow-lists,” or “require specific labels/annotations.”

• OPA/Rego for richer constraints that reference external data (e.g., a list of approved signers or minimum SLSA level), enforce signature and attestation verification, mandate presence of network policies in certain namespaces, or check SBOM metadata.

Deny messages reference the underlying invariant (“violates I_PRIV”) and, when relevant, the digest of the proof or counterexample artifact for traceability.

D. Evidence Model: “No Evidence, No Exposure”

All governance-relevant evidence travels with the release as signed attestations: build provenance (SLSA), SBOMs (SPDX or CycloneDX), signature verification records, proof summaries, and gate decision traces. Admission and promotion require the presence and validity of this evidence bundle. In practice, attestation formats are produced in CI, signed (e.g., with Sigstore), stored in an artifact registry, and referenced from deployment manifests.

E. Progressive Delivery and Rollback

Promotion follows a progressive exposure strategy (e.g., canary steps) under error-budget discipline. Advancement requires: (i) invariants continue to hold for the staged configuration; (ii) service-level indicators (latency, error rate) remain within configured envelopes relative to baseline; and (iii) evidence checks validate. Any breach triggers automatic rollback and creates a post-deployment issue with the decision trace and suggested recourse.

V. Implementation Blueprint (Kubernetes-Native)

CI (e.g., GitHub Actions) executes unit/integration tests, static analysis, specification checks, SBOM generation, provenance creation, and artifact signing. GitOps (e.g., Argo CD) maintains declared desired state; syncs are gated on passing evidence checks. In the cluster, ValidatingAdmissionPolicy enforces low-latency baseline constraints, while OPA Gatekeeper applies parameterized organization-wide rules and performs periodic audits to flag drift. The evidence registry stores attestations; deployments reference the set of digests used to justify the gate decision, enabling later replay.

A minimal starter pack—suitable for the first iteration—includes: (1) I_PRIV (deny privileged pods in compliance-scoped namespaces), (2) I_PROV (require valid image signatures, SLSA provenance, and SBOMs for production deployments), and (3) I_PII (no data with sensitive labels flows across designated namespace boundaries without approved transformation). Each is small, testable, and prevents a class of high-impact incidents.

VI. Evaluation Protocol

A rigorous evaluation should demonstrate prevention, latency bounds, and auditability without degrading delivery performance.

Scenarios. Use representative infrastructure-as-code and Kubernetes manifests seeded with common misconfigurations (privileged containers, missing network policies, insecure annotations), artifact sets with and without valid provenance/signatures/SBOMs, and small behavioral models with seeded violations. Where possible, replay historical incidents to assess whether the guardrails would have prevented exposure.

Metrics.

1. Violation prevention: fraction of seeded violations prevented pre-merge and at admission; false-negative rate.
   
   
   

2. Admission latency: additional p95/p99 time added by admission policies (CEL and Rego-based). For in-process CEL checks, targets on the order of single-digit milliseconds are reasonable; heavier checks can run in audit or pre-sync hooks.
   
   
   

3. Change-failure rate and rollback rate: relative to a baseline pipeline without formal checks.
   
   
   

4. Audit replay success: percentage of deployments for which gate decisions can be reproduced from the stored evidence.
   
   
   

5. Time-to-recourse: mean time from a denial to a passing re-submission, using counterexample guidance.
   
   
   

Baselines. Compare against (a) rules-only enforcement (policy-as-code without formal specs), (b) audit-after-deploy scans, and (c) verification-only without runtime enforcement.

Threats to validity. Over-abstract specifications may miss real behaviors; mitigate by keeping models small but targeted and by iterating templates with incident feedback. Incomplete evidence generation (missing SBOM or provenance) can cause false blocks; mitigate with “fail closed” defaults and clear remediation steps. Admission latency can grow with policy complexity; mitigate by separating low-latency CEL checks from richer Gatekeeper audits.

VII. Illustrative Case Study

A platform operates “dev,” “staging,” and “prod.” The organization adopts three invariants:

• I_PII (Data-flow). No object with label pii=true may flow from ns=ai-prod to ns=analytics unless a tokenization job is present in the path.

• I_PRIV (Privilege). No pod in namespaces matching pci-* may set securityContext.privileged=true or request host-level capabilities.

• I_PROV (Provenance). Images destined for env=prod must present valid signatures from approved signers, an SBOM (SPDX or CycloneDX), and a SLSA-level provenance attestation at or above the configured threshold.

A pull request adds an analytics connector. Pre-merge, the data-flow spec produces a counterexample path that bypasses tokenization; the PR is updated to include the transformation step and re-checked. The build emits SBOMs and provenance, and artifacts are signed. At deployment, a mis-scoped configuration sets a pod to privileged in pci-payments; ValidatingAdmissionPolicy denies the resource with a precise reason. After correction, a canary rollout proceeds; promotion occurs only after SLIs remain within envelopes and evidence checks validate. An audit six months later replays the decision using the recorded attestation digests.

VIII. Discussion

Why “lightweight”? The evidence from practice is clear: small, focused specifications pay for themselves by preventing high-impact defects early, whereas monolithic models are brittle and costly. The approach here limits specifications to properties that are both high-value and amenable to concise modeling, then connects them to runtime enforcement and provenance.

Operator experience. Denials include binding constraints and counterexamples, enabling “recourse by construction.” Typical edits include adding a missing network policy, rotating to an approved cipher suite, or attaching a required attestation. This keeps developer friction low while improving safety.

Limitations. Some properties (e.g., fairness of model outputs) are not readily expressible as crisp invariants and require human review; the design supports attaching documented sign-offs as attestations. Environmental nondeterminism and partial observability can limit model realism; specifications should be conservative and revised with incident learning.

IX. Conclusion

Formal-methods–integrated CI/CD offers a pragmatic path to decision-time assurance: compile a small set of high-leverage invariants, verify them automatically where they matter, enforce the corresponding policies at admission and promotion, and ship the evidence with the build. Coupled with progressive delivery and established supply-chain practices, this blueprint prevents a broad class of defects and policy violations without sacrificing velocity. For safety-critical and AI-enabled systems, “no evidence, no exposure” is a credible and achievable delivery invariant.

References

[1] C. Newcombe, T. Rath, F. Zhang, B. Munteanu, M. Brooker, and M. Deardeuff, “How Amazon Web Services Uses Formal Methods,” Communications of the ACM, vol. 58, no. 4, pp. 66–73, 2015.

[2] L. Lamport, Specifying Systems: The TLA+ Language and Tools for Hardware and Software Engineers. Addison-Wesley, 2002.

[3] D. Jackson, Software Abstractions: Logic, Language, and Analysis, 2nd ed. MIT Press, 2012.

[4] G. J. Holzmann, The SPIN Model Checker: Primer and Reference Manual. Addison-Wesley, 2003.

[5] E. M. Clarke, O. Grumberg, and D. A. Peled, Model Checking. MIT Press, 1999.

[6] B. Beyer, C. Jones, J. Petoff, and N. R. Murphy (eds.), Site Reliability Engineering: How Google Runs Production Systems. O’Reilly Media, 2016.

[7] N. R. Murphy, D. Rensin, B. Beyer, and C. Jones (eds.), The Site Reliability Workbook: Practical Ways to Implement SRE. O’Reilly Media, 2018.

[8] National Institute of Standards and Technology (NIST), Secure Software Development Framework (SSDF) Version 1.1, Special Publication 800-218, 2022.

[9] R. Chandramouli, Strategies for the Integration of Software Supply Chain Security in DevSecOps CI/CD Pipelines, NIST Special Publication 800-204D, 2024.

[10] NIST, Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023.

[11] ISO/IEC, ISO/IEC 42001:2023 — Artificial Intelligence Management System — Requirements, 2023.

[12] Open Policy Agent Project, “Open Policy Agent (OPA) and the Rego Policy Language,” CNCF project documentation/white paper, 2021–2024.

[13] The Kubernetes Authors, “ValidatingAdmissionPolicy: General Availability in Kubernetes v1.30,” release documentation, 2024.

[14] OpenSSF, Supply-chain Levels for Software Artifacts (SLSA) — Specification v1.0, 2023.

[15] SPDX Workgroup, Software Package Data Exchange (SPDX) Specification, Version 2.3. The Linux Foundation, 2022.

[16] OWASP Foundation, CycloneDX Bill of Materials (BOM) Specification, Version 1.5, 2023.

[17] S. Torres-Arias, H. Wu, I. Loh, R. Curtmola, and J. Cappos, “in-toto: Providing Farm-to-Table Guarantees for Every Bit,” in Proc. 28th USENIX Security Symposium, pp. 1393–1410, 2019.

[18] Sigstore Project, Sigstore: Design and Architecture for Software Artifact Signing and Verification, white paper, 2022.