AI-generated code and open-source dependencies both introduce the same core problem: code entering your pipeline without a verified, accountable source. Code signing addresses this by requiring every artifact, regardless of whether a human, an AI tool, or a third-party package wrote it, to pass through an accountability gate before it can be signed and released.
In short: neither AI-generated code nor open-source dependencies should reach production without passing through the same signing gate as human-authored code: dependency and vulnerability scanning (SBOM/SCA), human review of AI-authored changes, and a signature that only gets applied after both checks pass.
Key Takeaways
- AI-generated code and open-source dependencies fail differently: AI-authored code risk is about accountability and correctness (no human verified the logic); open-source dependency risk is about provenance (you can’t verify who maintains or last modified it). Both need to be blocked from the same signing gate, but for different reasons.
- Speed and security aren’t inherently in tension here: automated SBOM/SCA scanning and signing gates add pipeline stages, not manual bottlenecks, once integrated into CI/CD.
Risk Source to Control Mapping
| Risk Source | Specific Problem | Control |
|---|---|---|
| AI-generated code | No human verified the logic is correct or free of unintentionally introduced vulnerabilities | Require human review and approval before an AI-authored commit can be signed; log the reviewer’s identity in the signing audit trail |
| Open-source dependencies | Provenance and maintainer identity can’t be verified from the package alone | SBOM generation and SCA (software composition analysis) scanning before signing; block signing on known-vulnerable or unverified dependencies |
| Both | Pressure to ship fast can bypass either check | Make the signing step itself enforce the gate: if SBOM/SCA hasn’t passed or review hasn’t occurred, the pipeline can’t produce a valid signature |
Understanding CI/CD Pipelines and Code Signing
Code signing is a relatively easy process to understand, and it starts with a Public Key Infrastructure or PKI. The signer will request a code signing certificate from the PKI, thus receiving the public-private key pair they can use to sign the code. The key pair involves a public key, which is mathematically linked to the private key. The public key is a key that anyone can see and utilize, while the private key is one that only the certificate holder can utilize. The point of this code signing certificate is that the signing of the code is associated with the specific user who owns that certificate.
The actual code signing process involves hashing the code first. Hashing is a process where code or a document is fed into a hashing algorithm. This algorithm then converts the file into a hash digest, which is unreadable. Similar to encrypting, the hash digest is an unreadable mix of letters and numbers; however, unlike encryption, hashing is one-way. This means the hash digest can never be reversed to get the original text.
This hash is then sent to a Hardware Security Module (HSM) that stores the private key to the code signing certificate, and that private key is used to generate a signature for the code. The signature is then sent back to the client and bundled with the code. This signature ensures that the signer authenticates that the code is free of malware or viruses.
Now that we understand how code signing works, where does it work in the CI/CD pipelines that software developers use? A CI/CD pipeline utilizes multiple tools for each phase of the software development lifecycle, which automates and simplifies the software development process. Tools like Jenkins can be incorporated with code signing to create a digital signature as the final step.
Tools like SBOM can be used before code is put into GitHub to check for a specified number of vulnerabilities. Virus scanners can also scan code for viruses throughout the process, thus giving end-users faith in their computer software. As you can see, tools like AI or open-source tools could be a great way to speed up your CI/CD pipeline, but using these tools means you are compromising security for speed, which is never recommended.
Threats Faced by Open Source and AI Tools
Open-source or AI tools are great ways to improve and speed up your CI/CD pipeline, but many security concerns lie in these new types of tools. There are issues with the safety of open-source tools. Since the tools are open source, you cannot verify where they are from or who has changed or updated them. This causes issues with security because you want to provide software that an end-user can be confident has been securely created and managed.
Open-source tools can open flaws in a CI/CD pipeline, allowing threat actors to have new attack vectors for infecting victims with malware and viruses. AI-generated code introduces a distinct risk: it can be syntactically correct and pass basic tests while still containing logic errors, insecure patterns, or subtly incorrect assumptions that no human has actually reviewed. Treating AI-authored code as equivalent to human-authored code, without a review step specifically flagging it as AI-generated, removes that accountability.
The practical fix isn’t choosing speed over security or vice versa. It’s making the checks automated: SBOM generation and SCA scanning run in seconds as a pipeline stage, and flagging AI-authored commits for review doesn’t need to slow the overall release cadence if the review queue is kept current. The bottleneck is usually an absent gate, not an inherently slow one.
Frequently Asked Questions
Does code signing verify that AI-generated code is correct?
No. Code signing verifies origin and integrity, not correctness or security of the logic itself. It confirms who signed the code and that it hasn’t changed since. Catching flawed AI-generated logic requires human review and testing before the signing step, not the signature itself.
Can SBOM and SCA scanning fully replace manual review of open-source dependencies?
They catch known vulnerabilities and flag suspicious or unmaintained packages, but they can’t verify a maintainer’s identity or intent. Combining automated scanning with periodic manual review of critical dependencies gives more complete coverage than either alone.
Conclusion
Finding that balance of speed and security is very important and can be a difficult task for a smaller team. Luckily, Encryption Consulting can help. We can help ensure that your compliance needs are met, as well as your code signing needs. Our platform, CodeSign Secure, provides a number of different resources that you will use within your CI/CD pipelines.
Along with code signing, CodeSign Secure allows you to use its SBOM integrations to scan code for vulnerabilities before uploading it to GitHub. You can also integrate with your existing CI/CD pipelines, such as Jenkins, TeamCity, or Azure DevOps. To learn more about CodeSign Secure, reach out to us at www.encryptionconsulting.com.
