Developers are coding faster than ever. But are they coding safer?
Generative AI has transformed software development. Developers now complete tasks in hours that once took days, guided by copilots that autocomplete functions, generate tests, and even write documentation. Productivity has surged.
However, there’s a hidden tradeoff: as velocity increases, visibility decreases. AI code assistants like GitHub Copilot, Cursor, and Replit AI have quietly introduced a new class of software risk, one that’s invisible to traditional AppSec tools and policies. Organizations are now discovering that the same copilots boosting developer speed are also accelerating vulnerability creation.
The New Speed Problem: AI Writes, But Security Can’t Keep Up
Today’s SDLC wasn’t built for autonomous code generation. When copilots produce hundreds of lines of code per minute, conventional post-commit scanning simply can’t keep pace. Even the fastest pipelines only catch risks after they’ve been merged, at which point developers have already moved on, context has been lost, and remediation costs spike by 10–20x.
The result? A productivity–security paradox:
| Productivity | Security |
| Code velocity has increased 3–5x through AI assistance. | Code review coverage has dropped. |
| Developers trust AI suggestions by default. | 67% of orgs report no security oversight over AI tool usage. |
| GenAI tools boost delivery metrics. | They also import hallucinated dependencies and unsafe API patterns. |
The enterprise takeaway: AI is writing more code, but not necessarily better code.
The Hidden Risks Inside AI-Generated Code
AI assistants are probabilistic engines — they don’t reason, they predict. That means every completion is a guess based on prior patterns, not a guaranteed secure implementation. Here’s where the cracks form:
- Hallucinated Dependencies: AI can “invent” packages that don’t exist or import outdated, vulnerable libraries.
- Insecure API Patterns: Code suggestions often bypass authentication or error handling in subtle but dangerous ways.
- Policy Violations: Developers may use unapproved AI tools or generate code from unvetted sources, creating compliance drift.
- Hidden Data Exposure: Some AI tools send snippets of proprietary code to external models for context, a governance nightmare in regulated industries.
- Blind Spots in Tooling: Traditional SAST, SCA, and DAST tools can’t distinguish between human and AI-authored logic, leaving gaps in intent validation.
Why Scanning Alone Won’t Solve It
Even with robust automation, scanning happens after creation. AI has already moved on.
Organizations that rely solely on scanning are securing yesterday’s code, not today’s. By the time a vulnerability is flagged, that line of code may have been copied, merged, or reused across multiple branches.
What’s needed now is prevention at generation time: An intelligent agent that can reason over intent and context, that lives in the IDE, and that stops risky logic before it ever reaches the repo.
The Emergence of AI Code Security Assistance (ACSA)
AI is now writing as much code as developers, accelerating velocity while introducing entirely new classes of risk. AI coding assistants like GitHub Copilot, Cursor, and Replit AI are now embedded across enterprise IDEs, generating millions of new lines of code daily. Yet traditional AppSec tools were never designed to secure code written at machine speed.
What Is ACSA?
AI Code Security Assistance (ACSA) is the discipline Gartner identifies as the future of secure software development. Unlike traditional scanners that react after the fact, ACSA platforms assist developers during creation, validating every line of code as it’s generated. They bring security to the developer’s cursor, continuously validating both AI-generated and human-written code in real time.
To explore ACSA fundamentals and its enterprise definition, read our companion article:
What Is ACSA? Defining AI Code Security Assistance for the Enterprise
The Checkmarx Perspective: From Reactive to Agentic Security
Checkmarx One Assist was purpose-built for this shift. It’s a truly agentic AppSec platform that lets developers code at AI speed, safely and transparently.
Here’s how it bridges productivity and protection:
- Developer Assist validates logic inline in the IDE, blocking unsafe completions and providing explainable, context-aware fixes.
- Policy Assist enforces AI behavior and policy compliance before code ever leaves the developer’s environment.
- Insights Assist turns security telemetry into measurable business outcomes, from MTTR to AppSec ROI.
By moving enforcement left of the commit to the point of generation, Checkmarx delivers both the velocity developers want and the assurance enterprises demand.
From AppGenSec to ACSA
Analysts now describe the next phase of AppSec as the convergence of two forces:
- Forrester’s AppGenSec, emphasizing proactive, generative security that evolves with AI-driven development.
- Gartner’s ACSA, emphasizing agentic validation and policy enforcement at generation time.
Together, they define the future of software security: AppGenSec powered by ACSA. Checkmarx One Assist operationalizes this model, in which autonomous agents reason, remediate, and report in real time.
The Bottom Line: Security Must Evolve as Fast as AI
As copilots redefine how code is written, AppSec must redefine how it protects.
Enterprises that evolve now will not only reduce vulnerabilities but gain sustainable velocity, while those that don’t will drown in the technical debt of AI-written code.
Security can’t chase the code anymore. It has to work with it.
Further Reading
- The Future of AppSec in the Era of Agentic AI and ACSA: How these frameworks are reshaping enterprise security.
- Gartner: AI Code Security Assistance Report: Analyst insight into this emerging category.
- Visit Checkmarx One Assist to book a demo and experience the future of AppSec.