Snyk adds an independent security check to AI-driven development

Snyk combines code and dependency analysis with agent security, while enterprise billing counts monitored activity across capabilities.

01 / ProductSecurity spans the codebase and the agents changing it

Snyk is a developer-security company whose platform connects static application testing, open-source dependency analysis, container scanning and infrastructure-as-code checks. Its newer Evo offer addresses AI applications and coding agents. These layers answer different questions: whether custom code contains a vulnerable flow, whether an imported package is known to be vulnerable, and whether an agent should be allowed to use a tool or perform an action. A clean result in one layer does not establish that the others are safe.

DeepCode AI supplies security-focused analysis and fix suggestions, combining symbolic and generative methods according to Snyk. Snyk Code places static analysis in the developer workflow. The practical distinction from a code generator is that the security system evaluates the resulting program against a different body of rules and security context. This is a useful division of responsibility, although neither separate branding nor a suggested fix proves that the application is secure.

Evo Agentic Development Security extends that scope to MCP servers, skills, external tools and generated code. Its behavior-governance component is explicitly marked open preview on the current product page. Snyk remains the company identity here; DeepCode and Evo are parts of its offer, not additional companies. This blueprint is based on public documentation and commercial pages, without a hands-on detection or agent-control assessment.

02 / AudienceStart with the security decision developers repeatedly postpone

A good candidate is a team whose coding agents generate useful patches faster than application-security reviewers can inspect them. The immediate need may be modest: identify a dangerous data flow, explain it to the developer and check the proposed correction before merge. That is a narrower and more measurable adoption goal than expecting one product to govern every AI workflow across the organisation.

A platform team with multiple repositories faces a different problem. It needs consistent policies and visibility into dependencies, images and infrastructure, while preserving service ownership. Snyk is relevant when findings can be assigned to the developer who can actually change the affected artifact. Importing repositories without ownership and exception rules can produce a larger queue without reducing the time that vulnerabilities remain exposed.

Compare GitLab when the decision is whether security controls should sit within a broader software delivery platform. Compare CodeRabbit when pull-request review and developer feedback are the main bottleneck. These products have overlapping workflow surfaces, but a general review comment, a vulnerability finding and an enforced agent policy represent different outcomes. Evaluate the outcome you need instead of treating every AI-assisted code tool as interchangeable.

03 / WorkflowProposed workflow: review one agent-generated change through to remediation

Select a representative service and a small set of previously reviewed changes. Include a straightforward dependency upgrade and a custom-code change involving untrusted input. Establish the intended application behavior and the existing tests before enabling additional automation. This proposed pilot evaluates whether Snyk adds actionable security evidence; it is not a report of a production deployment.

Connect the repository through an approved integration, then choose which Snyk capabilities should monitor it. Keep the inventory explicit: source projects, package manifests, container images and infrastructure definitions are separate artifacts. A repository that builds several services may require more than one project. Confirm that the languages and build layout used by this service are supported before interpreting an empty findings list.

Have the developer inspect a finding in context. Trace the affected input, vulnerable operation and relevant execution path, then compare the proposed fix with the service contract. Run the application tests and a targeted regression case after applying a correction. A patch that removes a reported issue by breaking the feature is not a useful remediation, even if the original finding disappears.

If the pilot includes Evo, begin with its documented agent-supply-chain inventory and a tightly scoped policy. Record the approved MCP servers and skills for the service, and test an authorised and an unauthorised tool choice in a non-production environment. Treat preview behavior controls as a separate evaluation with their own acceptance criteria, rather than an assumed foundation for a mandatory production gate.

Keep unresolved findings with the service owner, expiry date and explanation for any exception. Measure the percentage of findings that lead to a verified correction, the time required to review fixes and the cases where the tool lacks enough context. Also record monitored days and consuming identities. That connects the security result to the new enterprise charging model instead of estimating cost from the number of scans alone.

04 / PricingEnterprise credits follow monitored activity, including non-human contributors

The current plans page separates Free and Team subscriptions from Enterprise Platform Subscription. It shows Free at $0 and Team starting at $25 per month, billed monthly, for teams of up to ten developers. Evo is excluded from both entry plans. Enterprise uses prepaid credits, with a published rate card defining one credit as $1; a sales agreement establishes the commitment.

The billing policy makes the measurement especially important for agent-heavy teams. An active contributor can be a human, bot, service account or agent with a commit to a monitored private repository during a rolling 90-day period. Monitoring can consume credits even on a day without a scan, and a partial monitored day counts as a full day. These are operational license units, not simply purchased seats.

Before comparing a legacy Snyk quotation with the current platform, obtain the actual order and capability mapping. The live public plan has changed from older indexed descriptions, so old per-developer summaries are not a reliable enterprise estimate. Also confirm how alerts, top-ups and on-demand usage are controlled: the billing policy says use beyond the prepaid balance is invoiced, rather than automatically stopping protection.

05 / DifferenceA separate validator can make AI-generated changes easier to review

Snyk’s useful distinction is the range of software artifacts it can assess inside the development lifecycle. A developer can encounter a risky dependency and a custom-code flaw in the same change, while a platform owner cares about the image that will run it. Keeping these findings linked to the delivery process helps reviewers understand which team can fix which layer. It does not make source scanning equivalent to runtime monitoring.

The newer agent controls also change the unit of analysis. Reviewing a generated patch happens after an agent has already selected inputs and tools. Inventorying those tools and examining actions brings part of the security question earlier in the workflow. This is particularly relevant when several coding agents coexist, but the value depends on real coverage of the clients, versions and execution environments that developers use.

06 / LimitsValidate coverage and fixes before treating a finding as a release decision

Ask Snyk to demonstrate a finding and its correction using the team’s own language, framework and repository structure. Confirm what code context is sent outside the environment, which deployment route is supported and whether the chosen integration can reach private dependencies. A product page describing AI analysis does not settle the organisation’s permitted data flow.

For agent governance, distinguish inventory from enforcement and stable controls from open preview. The pilot should record an attempted action, the applicable policy and the observed outcome. If a control only reports activity after execution, a team cannot rely on it as a preventive boundary. Likewise, fix suggestions need functional regression checks and review of newly introduced dependencies; the vendor’s accuracy claims are not evidence of performance on this codebase.

07 / DecisionChoose the first enforced boundary deliberately

Snyk is worth evaluating when security findings can become verified changes in a real engineering workflow. Begin with a well-understood service, prove that the findings and fixes help its maintainers, and expand coverage using measured artifact and identity counts. Agent governance can then be assessed on its own documented availability and actual enforcement behavior, with a clear owner for exceptions.