TL;DR
- The AI-native SDLC redesigns the software lifecycle around AI participation. The AI-assisted SDLC adds AI tools to stages that otherwise stay the same.
- AI-DLC usually means AWS's named methodology, not shorthand for "AI-driven SDLC."
- ADLC has two meanings. Endor Labs uses Agentic Development Lifecycle for the SDLC with autonomous coding agents working inside it.
- For AppSec, the shift means securing both the code agents write and the actions they take.
What is the AI-native SDLC?
The AI-native software development lifecycle (SDLC) is redesigned around AI participation. AI models, and often coding agents that carry out multi-step work, take part across planning, design, coding, testing, review, release, and maintenance. Teams adapt their workflows, handoffs, and controls to that role instead of adding AI tools to an unchanged process.
That definition separates two situations that often get lumped together. A developer who accepts autocomplete suggestions in an IDE is using AI in one stage. A team that writes specs for agents, routes agent-authored pull requests through a different review path, and tracks what agents changed has reshaped the lifecycle itself.
The vocabulary here is still settling. Vendors, researchers, and practitioners use overlapping labels with different emphasis, and the same acronym can mean two different things. This guide explains the main terms, compares them, and covers what the shift means for application security.
AI-assisted SDLC, AI-native SDLC, and ADLC compared
These terms differ mainly in how much the AI does: whether it helps a person finish a task, changes how the work flows, or acts as an autonomous participant.
The AI-assisted SDLC adds AI tools to stages that otherwise look the same. A sprint still runs the same way, reviewers still read every line, and the CI pipeline doesn't change. The AI-native SDLC changes the structure. Specs might become the main input to code generation. Review might focus on intent and test coverage instead of line-by-line reading. New controls might track which changes came from an agent.
Most teams today sit somewhere between the two.
Other names for the AI-native SDLC
Several related labels are in use. They overlap, but they aren't interchangeable, and none has a universal definition. The table below shows who uses each term, what it emphasizes, and how it relates to the AI-native SDLC.
AI-DLC isn't just shorthand for "AI-driven SDLC." When someone writes AI-DLC, they usually mean the AWS methodology, which has its own structure and vocabulary. If you mean the general idea, write "AI-driven SDLC" so readers don't assume you're describing the AWS approach.
AI SDLC has two meanings. Some sources use it for building software with AI tools. Others use it for the lifecycle of building, training, deploying, and monitoring AI or machine learning systems. The same goes for "AI development lifecycle." When you use either term, say which one you mean.
AI-native SDLC vs. ADLC: resolve the acronym
ADLC currently has two common expansions, and they describe different things.
Agentic Development Lifecycle is Endor Labs' framing. It describes the ADLC as the SDLC with autonomous actors operating inside it. The software being built can be anything: a payments API, a mobile app, a data pipeline. What changes is who does the work. Coding agents read code, edit files, install packages, run commands, and open pull requests. For a deeper explanation, see Endor Labs' guide to the secure agentic development lifecycle.
Agent Development Lifecycle refers to building and operating AI agents as products. IBM and Atlan both publish explainers using ADLC in this sense. The focus is on how to design, evaluate, deploy, and monitor an agent that customers or employees will use. This meaning sits closer to the "building AI systems" sense of AI SDLC.
How do the AI-native SDLC and the Agentic Development Lifecycle relate? The AI-native SDLC is a broad concept about redesigning software development around AI. The Agentic Development Lifecycle is a lens on that same lifecycle. It focuses on autonomous actors and what their actions mean for security. It doesn't replace the SDLC, and it isn't a universal standard. It names the part of the AI-native SDLC that security teams often have little visibility into today.
Architecting security into the agentic SDLC
What changes across the software lifecycle
AI participation can touch every familiar stage. How much it changes depends on the team, the codebase, and the risk of the change.
- Requirements and planning: Specs and tickets become direct inputs to code generation, so ambiguity in a ticket turns into ambiguity in the code. Teams often write tighter acceptance criteria because an agent will act on them literally.
- Design: Architecture decisions, interface contracts, and constraints get written down so agents can follow them. Conventions that people picked up informally need to be documented.
- Implementation: A coding assistant suggests code that a developer accepts or rejects. A coding agent executes a task. Review, testing, and security checks need to account for which of the two produced a change.
- Testing and verification: Agents can generate and run tests, but generated tests can share the same blind spots as generated code. Independent verification, including static analysis and human-designed test cases, stays important.
- Review and release: Pull request volume can climb faster than reviewers can keep up. Teams may change what review looks for, such as intent, test coverage, and security context, instead of reading every line with equal attention.
- Maintenance: Agents can handle dependency upgrades, refactors, and bug fixes. Each change still needs the same scrutiny as a human-authored one.
Consider a hypothetical example. A developer asks an assistant how to fix a deprecated logging call, gets a snippet, and pastes it in. Compare that to an agent that receives a ticket to upgrade the logging library. The agent edits 14 files, adds a new transitive dependency, runs the test suite, and opens a pull request. The assistant only suggested code, while the agent made a series of changes on the developer's behalf.
In both cases, people still review the result, but how closely depends on the task. A one-line copy change and a change to authentication logic shouldn't get the same review path. One argument, from a daily.dev post titled "The AI-native SDLC won't be one process," is that teams will run different workflows for changes with different risk levels instead of one fixed AI-native process.
What the shift means for AppSec
The AI-native SDLC gives AppSec two things to account for: the software agents produce and the actions agents take.
The software produced. AI-generated code can inherit flaws from its training data. Common examples include hardcoded secrets, unsafe deserialization, weak crypto, hallucinated APIs, and logic flaws that pattern-matching SAST can miss. Agents also pick dependencies. An agent that adds a package to solve a problem brings in that package's vulnerabilities, license terms, and transitive dependencies.
Not every vulnerability in that code or those dependencies affects the application. Reachability analysis asks whether a vulnerable function can actually be called from your application's code paths. A vulnerable function in a dependency is present. A vulnerable function that your code calls through a traceable path is reachable, and that's the one worth fixing first. As AI increases code and pull request volume, separating present from reachable is how teams keep findings manageable.
The actions agents take. Agents also run commands, change configuration, and touch systems outside the repository. Reviewing the final diff doesn't show everything that happened along the way. Endor Labs' guide to coding agent governance covers this side in more detail.
From Endor Labs' perspective, security for the AI-driven SDLC works best as continuous intelligence where code is written. That means checking code at the point of generation, reviewing every pull request with security context, and handing developers a fix instead of another ticket. It shouldn't be a scan that blocks the pipeline after the build. Endor Labs describes AURI by Endor Labs as combining agentic reasoning with deterministic program analysis and a code context graph to reason about security across code and dependencies. For more on how security fits inside this model, see the architectural shift behind the AI SDLC and our overview of AI code governance.
Governing agent actions raises its own questions, such as who owns the controls and where policy applies. Those questions get a full treatment in why application security needs coding agent governance.
FAQs
Is agentic SDLC the same as AI-native SDLC?
They're closely related, but the emphasis differs. AI-native SDLC describes a lifecycle redesigned around AI participation of any kind. Agentic SDLC focuses on agents that perform multi-step work inside that lifecycle. An agentic SDLC is AI-native. An AI-native SDLC doesn't have to rely on autonomous agents for every stage.
What is AI-DLC?
AI-DLC stands for AI-Driven Development Life Cycle, a methodology from AWS. IBM credits AWS's Raja SP with coining the term in 2025. In AI-DLC, AI drives execution while humans provide oversight and make decisions. Use "AI-driven SDLC" when you mean the general idea and "AI-DLC" when you mean the AWS methodology.
What is the difference between AI SDLC and ADLC?
AI SDLC is a broad umbrella for using AI across the software lifecycle. ADLC depends on the expansion. Endor Labs uses Agentic Development Lifecycle to mean the SDLC with autonomous actors operating inside it. IBM and Atlan use Agent Development Lifecycle to mean building and operating AI agents as products.
Does AI SDLC mean building software with AI or building AI systems?
It can mean either. In this guide, AI SDLC means building software with AI tools and agents. Other sources use AI SDLC, or AI development lifecycle, for the lifecycle of building, training, deploying, and monitoring AI or machine learning systems.
What's next?
When you're ready to take the next step in securing your software supply chain, here are 3 ways Endor Labs can help:
