The Future of Dependency Management in an AI-Driven SDLC

February 04, 2026
By Aaron Linskens

7 minute read time

AI coding assistants now power a growing share of modern software delivery. They span the SDLC, helping teams move faster from idea to implementation, expanding what individual developers can deliver, and accelerating release cycles across the enterprise.

We recently hosted a webinar, "Autonomous Development Meets Autonomous Security: The Future of Dependency Management," featuring Tyler Warden (Senior Vice President at Sonatype) and guest speaker Janet Worthington (Senior Analyst at Forrester).

The session explored a central tension facing modern engineering organizations: as development becomes more autonomous, security must become more autonomous, especially when it comes to dependency choices that define your software supply chain.

AI-Assisted Development Expands Across the Entire SDLC

AI's impact extends beyond code completion. Many organizations are adopting tools and agents that influence multiple phases of delivery, including:

• Requirements and user story drafting.
• Design support and architecture exploration.
• Coding, refactoring, and test generation.
• CI/CD workflow enhancements.
• Documentation and ticket triage.

This matters because security and governance cannot treat "AI coding assistants" as a single isolated tool category. As AI becomes present throughout the SDLC, the total surface area of automated decisions increases, often outside traditional review points.

Adoption Pressure Changes the Risk Profile

In many organizations, the decision to use AI is not grassroots. Adoption is frequently driven by leadership initiatives focused on velocity and efficiency.

That adoption pressure creates two immediate security challenges:

• Tool sprawl and inconsistent usage patterns: Different teams adopt different assistants, models, plug-ins, and workflows.
• Governance lag: Policy, risk management, and control frameworks often follow after adoption, rather than preceding it.

The result is AI becomes part of delivery before teams agree on what "safe AI use" looks like in practice.

More Code, More Change, More Review Load

AI-assisted development tends to increase overall output: more code, more commits, more pull requests, more changes flowing through pipelines.

Even when AI improves productivity, it also changes the economics of software maintenance:

• Larger codebases increase long-term maintenance costs.
• More frequent changes increase review and testing pressure.
• Traditional code review expectations may not scale linearly with output.

This is where "autonomous development" creates a paradox: teams get faster at producing change, but existing quality controls can become bottlenecks unless they also evolve.

The Hidden Problem: AI Accelerates Dependency Decisions

The biggest supply chain shift isn't just that AI generates code. It's that AI can recommend libraries, frameworks, and versions at the moment a developer implements a solution.

That changes dependency management in three important ways.

Dependency Selection Happens Earlier

Instead of choosing dependencies after design discussions or team conventions, developers may accept suggestions while coding in the IDE. That pulls supply chain decisions closer to the keyboard, and often earlier than established guardrails.

Recommendations May Be Stale or Misaligned

Model knowledge can lag behind current library health, security posture, end-of-life status, and best-practice guidance.
That can introduce:

• Outdated libraries or versions.
• Insecure patterns embedded in older examples.
• Dependencies with known vulnerabilities.
• Packages with incompatible licensing.

Attackers Can Exploit Suggestion Dynamics

When development becomes suggestion-driven, risk shifts toward:

• Typosquatting and lookalike packages.
• Malicious packages seeded to match common prompts.
• Dependency confusion and registry manipulation.
• Supply chain compromise via maintainers or popular ecosystems.

The key takeaway: AI increases the speed and frequency of dependency decisions, and that makes dependency governance more critical, not less.

Autonomous Security Starts With Enforceable Guardrails

The webinar's central theme applies directly to dependency management: if development becomes more autonomous, security controls must operate closer to the point of change.

Practically, that means shifting from "detect later" to "control earlier," including:

• Policy-based dependency selection: Only approved versions, vendors, licenses, and risk thresholds.
• Preemptive vulnerability prevention: Block risky components before they enter builds.
• Automated validation: Continuous checks that don't rely on manual review bandwidth.
• Consistent enforcement across teams: Guardrails that work whether code is human-written or AI-generated.

The goal isn't to slow teams down. It's to keep pace with increased change volume.

SBOMs: Visibility Is a Requirement, Not a Nice-to-Have

As software supply chains grow more complex, and as regulations evolve, teams need reliable visibility into what they ship.

A software bill of materials (SBOM) provides a structured inventory of application components, including direct and transitive dependencies.

SBOMs enable teams to:

• Identify vulnerable components quickly.
• Assess transitive exposure.
• Track operational and license risk.
• Support compliance and attestation requirements.
• Respond faster during incident response.

In a world where dependency choices can be instantly introduced through AI-assisted workflows, SBOMs become foundational infrastructure for governance, not just a reporting artifact.

AIBOMs: Extending the Bill of Materials Concept to AI

As AI becomes embedded in products and development workflows, a parallel need emerges: visibility into AI systems themselves.

An AI bill of materials (AIBOM) is an emerging concept aimed at documenting key elements of AI systems, such as:

• The model(s) in use.
• Relevant datasets or training provenance (where applicable).
• Supporting libraries and dependencies.
• Deployment configuration and integrations.

For organizations operating in regulated environments — or selling into customers with security expectations — AIBOMs are part of building auditable trust around AI.

Practical Steps for Teams Adopting AI-Assisted Development

If AI coding tools and agents enter your SDLC, dependency management and AppSec teams can reduce risk without blocking progress by focusing on a few pragmatic moves:

• Map where AI is used (IDE assistants, CI agents, ticket tooling, internal platforms).
• Define what "approved dependency usage" means (versions, licenses, sources, risk tolerances).
• Move dependency controls earlier (at selection time, not after release).
• Automate enforcement so review practices can scale with output volume.
• Treat AI-generated code like any other code: same quality standards, same security gates.
• Operationalize SBOMs for continuous monitoring, not just compliance artifacts.
• Begin planning for AIBOM requirements as customers and regulations mature.

The Next Era of Dependency Management

AI is accelerating development, but it also compresses the distance between "idea" and "dependency decision." That makes software supply chain governance a first-class requirement for any organization aiming to adopt AI responsibly.

The future of dependency management is not just faster scanning or more alerts. It's intelligent automation paired with enforceable policies — security that can operate at the same speed as modern development. Autonomous development requires autonomous security.

Want a deeper look at what this shift means for AppSec and software supply chain teams? Watch our on-demand webinar to learn how organizations can balance AI-driven development with automated, scalable security controls.

Written by Aaron Linskens
Aaron is a technical writer at Sonatype. He works at a crossroads of technical writing, developer advocacy, and information design. He aims to get developers and non-technical collaborators to work better together in solving problems and building software.