Ship Faster, With Guardrails
AI pair-programming keeps velocity high. Gravio makes sure speed does not silently trade away reliability, security, or governance.
WHY GRAVIO
AI can ship code in minutes.
Incidents can happen just as fast.
Gravio gives teams a minimal oversight layer for AI-assisted development: continuous quality scoring, policy checks, and secure trend visibility across your full codebase.
See Risk Before Production
Score trends and failed checks surface weak spots early, so teams fix regressions in CI windows instead of incident response windows.
Create Trust With Evidence
Use concrete scan history and policy outcomes to align engineering leaders, security teams, and customers around quality posture.
Why oversight matters now
Industry and security guidance is increasingly clear: AI-generated code without review, testing, and governance can introduce exploitable and expensive failures. Gravio operationalizes those controls without adding workflow drag.
- Kaspersky: Examples of vibe-coding abuse and manipulation risks in AI-generated software workflows.
Read Kaspersky analysis - OWASP: The Top 10 risks for LLM applications, including prompt injection and insecure output handling.
Read OWASP Top 10 for LLM Apps - NIST: Formal AI risk management framework emphasizing governance, measurement, and continuous monitoring.
Read NIST AI RMF - MITRE ATLAS: Knowledge base of adversarial techniques targeting AI-enabled systems and pipelines.
Read MITRE ATLAS
The numbers behind the risk
Every figure below is from a named public source and links directly to the original study or report. All figures reflect research published 2024–2025.
45%
AI-generated code samples failing security tests
↑ 2–3× higher than comparable human-reviewed code
Veracode tested outputs from 100+ LLMs across 5 languages. Nearly half contained at least one security weakness. XSS alone failed 86% of the time. Newer LLM models were no more secure than older ones.
37%
More critical vulnerabilities after 5 AI revision cycles
↓ Drops 50% when security focus is added to prompts
Iterating on AI-generated code without guardrails compounds risk. A GPT-4o study found that each revision cycle added critical vulnerabilities unless prompts were explicitly security-focused — highlighting the need for continuous checking, not just initial review.
1 in 5
Organisations with a systemic security exposure from AI-written apps
4 recurring patterns: client-side auth, hardcoded secrets, open DB tables, unauthorised internal access
Wiz reviewed production AI-generated applications across hundreds of organisations and found 20% had at least one severe, systemic exposure — not from novel exploits but from the same four well-known vulnerability classes, consistently reproduced by AI tools.
82%
Developers actively using AI coding assistants at work
↑ From ~45% the prior year — adoption is not slowing
Among 65,000+ respondents in Stack Overflow's 2024 survey, 82.1% use ChatGPT and 41.2% use GitHub Copilot regularly. Governance is now the differentiator: most organisations have already adopted; the question is whether controls kept pace.
56%
Faster task completion reported with AI coding tools
↑ McKinsey projects 20–45% broader software engineering productivity impact
The productivity case for AI coding is settled. The strategic question is how to capture gains sustainably — without accumulating hidden security debt or quality regressions that cancel the speed advantage and create larger remediation costs later.
72%
Java AI code samples failing — the highest of any language tested
Python 38% · JavaScript 43% · C# 45% — elevated across all
Failure rates differ by language but are consistently above typical baselines across all five Veracode tested. Targeting security in prompts and applying systematic review reduced failure rates significantly — but neither alone eliminated the gap.
Security test failure rate: AI-generated vs. estimated baseline, by language
Percentage of code samples failing at least one security test. AI-generated figures from Veracode 2025 (100+ LLMs). Baseline is an estimated industry average for code entering static analysis without AI assistance, based on published SAST defect density benchmarks.
What Gravio solves
Most teams either move fast without controls or add heavy governance that kills momentum. Gravio is the middle path: lightweight, continuous, and developer-native.
Gravio provides repeatable multi-dimensional scoring and trend history, so quality moves from opinion to measurable signal.
Checks are evaluated uniformly across projects, reducing human variance and hidden drift between teams.
Use account-scoped access controls and optional E2EE to keep visibility high while preserving data boundaries.
Built to be understood by humans and AI overviews
This page is intentionally structured for GEO and AIO: explicit problem statements, evidence links, concise claims, and FAQ answers that search systems can quote accurately.
What is Gravio in one sentence?
Gravio is a codebase quality engine that adds continuous oversight to AI-assisted software delivery without slowing teams down.
Who should use Gravio?
Engineering teams using AI coding assistants who need speed plus measurable quality, security, and governance outcomes.
How quickly can teams adopt it?
Teams can start with the guided onboarding flow in minutes, run scans immediately, and use dashboard trends for ongoing improvement.
CONVERSION
Keep your AI velocity.
Remove the blind spots.
Start free and get a baseline quality signal for your codebase today.