AI Code Security Services
Your engineering team adopted AI coding tools months ago. Productivity went up. But nobody asked what happens when that AI-generated code introduces security vulnerabilities your existing tools weren't designed to catch.
We build security pipelines specifically designed for AI-generated code. Pre-commit scanning that runs inside your IDE, static analysis calibrated for AI output patterns, automated secret detection, and compliance verification on every pull request. The pipeline integrates into your existing CI/CD workflow and catches problems before they reach production.
Siblings Software is a software outsourcing company based in Miami, Florida, with engineering teams in Argentina working in US time zones. We've been building outsourced software teams since 2014.
Why AI-Generated Code Needs Different Security
AI coding tools produce syntactically correct code most of the time. The problem is what sits underneath the syntax. Models like Copilot and Cursor were trained on public repositories, including repositories with known vulnerabilities. When Copilot suggests a database query, it might use string concatenation instead of parameterized queries. When Cursor generates an authentication flow, it might hardcode a placeholder API key that looks intentional but should never ship.
Traditional SAST tools catch some of this, but they generate excessive false positives on AI code because the patterns are different from what human developers typically write. Security teams at companies we've worked with report that AI-generated code produces roughly double the false positive rate of human-written code. That leads to alert fatigue. Dashboards fill with noise. And real vulnerabilities slip through.
The OWASP Foundation has documented how AI code generators reproduce the same vulnerability categories that humans struggle with (injection, broken authentication, sensitive data exposure) but at higher volume and velocity. The difference is scale: a developer might write one insecure query handler per week. An AI tool might generate a dozen before lunch.
We don't think the answer is restricting AI tool usage. That ship has sailed. Over 80 percent of professional developers use AI coding tools regularly, and the number keeps climbing. The answer is building security infrastructure designed for how code is actually being written now, not how it was written three years ago.
What We Build
Our AI code security practice covers five areas. Not every engagement includes all of them. It depends on your current security posture, your tech stack, and your compliance requirements.
Pre-Commit Scanning
IDE plugins for VS Code, Cursor, and JetBrains that flag vulnerabilities the moment AI generates code. Git hooks add a second gate. This catches SQL injection patterns, hardcoded credentials, and insecure deserialization before code reaches your repository. Developers get real-time feedback without switching context.
AI-Tuned Static Analysis
SAST configured specifically for AI-generated code patterns. We use tools like Semgrep with custom rule sets, calibrated to the specific AI tools your team uses. Copilot-generated Python has different vulnerability patterns than Cursor-generated TypeScript. We account for that, which means significantly fewer false positives.
Secret Detection
AI tools frequently generate placeholder credentials that look real. Our secret scanning goes beyond regex pattern matching. It understands context to distinguish test fixtures from real API keys. We also handle historical scanning and credential rotation for secrets already in your repository.
Dependency Analysis
Software composition analysis for every dependency. AI-generated code often pulls in outdated packages because that's what the model learned from. We scan for known CVEs, license compliance issues, and deprecated libraries that AI tools recommend without knowing they've been superseded.
Compliance Automation
Automated verification for SOC2, HIPAA, PCI-DSS, GDPR, and the EU AI Act. Every pull request is checked against your applicable compliance requirements. Non-compliant code gets blocked with specific remediation guidance, not vague warnings.
Our security work integrates with the platforms built by our Python and full-stack engineering teams, and draws on our AI-powered testing practice for quality assurance that goes beyond security scanning alone. For teams using AI coding agents at scale, our harness engineering practice provides the constraint and verification infrastructure that makes agent output reliable.
How an Engagement Works
Most engagements follow four phases over 10 to 14 weeks. Smaller scopes (a single team, one repository, focused tooling) can be fully operational in 4 to 6 weeks.
Phase 1: Security Audit (Weeks 1-2)
We review your codebase with specific focus on AI-generated segments. What tools is your team using? What percentage of recent commits came from AI assistance? Where are the vulnerability clusters? The output is a risk-prioritized inventory and a practical remediation plan.
Phase 2: Pipeline Design (Weeks 3-4)
Based on the audit, we design the security pipeline. Which scanning tools fit your stack? Where do security gates go in your CI/CD workflow? What compliance frameworks apply? How aggressive should blocking rules be? We make these decisions with your team, not for them.
Phase 3: Tool Integration (Weeks 5-10)
The build phase. We deploy scanning tools, configure SAST rules, set up secret detection, wire everything into your CI/CD pipeline (GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or CircleCI), and calibrate severity thresholds. Every tool is tuned to minimize false positives for your specific codebase.
Phase 4: Handoff and Training (Weeks 10-14)
Documentation, runbooks, developer security workshops, and operational training. The goal is your team running the pipeline independently. We are not trying to create a long-term dependency. If you want ongoing support after handoff, we offer that too, but it's not the default assumption.
The pipeline connects with your DevOps infrastructure, and for teams building AI products, our AI agents development practice provides complementary engineering depth.
How We Helped a HealthTech Company Secure AI-Generated Code
The Situation
A healthtech company with about 50 engineers came to us after a penetration test surfaced problems they hadn't expected. Their team had been using Copilot and Cursor for about six months. Productivity had improved noticeably, and nobody wanted to give up the AI tools.
But the pen test found hardcoded database credentials in three repositories, unencrypted patient data transmission in two API endpoints, and SQL injection vulnerabilities in patient query handlers. Most of these had been introduced by AI-generated code that developers accepted without security-specific review. The code compiled, passed basic tests, and looked clean. But it violated HIPAA requirements in ways that weren't obvious without targeted analysis.
Their compliance auditor flagged the platform for insufficient technical safeguards. The company faced a choice: halt AI tool usage entirely (and lose the productivity gain) or build security infrastructure that could keep pace with AI-assisted development. They chose to build.
What We Built
We deployed a security pipeline over 12 weeks with a six-person team: two application security engineers, two AI/ML engineers, one DevSecOps specialist, and a security architect leading the engagement.
The critical decisions were:
- IDE-level scanning that flagged PHI exposure and credential hardcoding in real time as developers accepted AI suggestions. This single layer caught the majority of new vulnerabilities before they even reached a commit.
- Custom Semgrep rules calibrated for the specific patterns Copilot introduces in Python and TypeScript codebases, dramatically reducing false positives compared to their previous SonarQube setup.
- HIPAA compliance checks on every pull request, blocking non-compliant merges automatically with specific fix instructions.
- Historical secret scanning with credential rotation for everything already in the codebase.
After deployment, the team continued using AI coding tools at the same pace. The difference was that vulnerable code stopped reaching production. Their HIPAA compliance audit the following quarter passed without findings. Want to see more of our work? Visit our case studies page.
When Outsourcing AI Code Security Makes Sense
Not always. Here's an honest breakdown.
Outsourcing Is a Good Fit When
- You don't have application security engineers with AI/ML experience on staff
- You need scanning operational in weeks, not quarters
- You're in a regulated industry and compliance deadlines are approaching
- Your current SAST setup is generating too much noise on AI-generated code
- You want to build the capability once, get it right, and hand it off to your internal team
Building In-House Makes More Sense When
- You already have a strong AppSec team that just needs AI-specific training
- Your development team is small enough (under 15-20 engineers) that manual review still works
- You have the time and budget (6 to 12 months) to hire and ramp specialized security engineers
The Cost Reality
Hiring a senior application security engineer, an AI/ML engineer, and a DevSecOps specialist in the US costs north of $600,000 per year in fully loaded compensation. And that's a minimal team. Our nearshore model delivers the same skill set at roughly 40 to 50 percent of that cost, with engineers working in your time zone.
For project-based engagements, typical AI code security pipeline builds range from $80,000 to $250,000 depending on scope, number of repositories, and compliance requirements. The NIST Cybersecurity Framework has useful guidance on evaluating whether your current security investment matches the risk profile of AI-assisted development.
Three ways to work with us, depending on what you need.
How to Work With Us
Project-Based
Outsourcing
We build the security pipeline end-to-end and hand it over. Best for companies that want production infrastructure without managing the build process. Typical duration: 10-14 weeks.
Dedicated
Security Team
An ongoing security engineering team embedded in your organization: security architects, AI/ML engineers, AppSec specialists, and DevSecOps engineers. They work as an extension of your team with full context on your systems.
Staff
Augmentation
Embed individual AI code security engineers into your existing team. Best when you have the security strategy defined but need hands-on expertise to build scanning pipelines, configure SAST engines, or implement compliance automation.
Frequently Asked Questions
All major languages: Python, JavaScript, TypeScript, Java, Go, Rust, C#, Ruby, PHP, and Swift. We also scan infrastructure-as-code files like Terraform configurations, CloudFormation templates, and Kubernetes manifests. The scanning rules are calibrated per language because AI tools introduce different vulnerability patterns in each one.
Usually not. We integrate with and extend your current setup. If you're running SonarQube, we add custom rules tuned for AI-generated patterns. If you're using Snyk for dependency scanning, we keep it and layer additional scanning around it. The goal is to enhance what you have, not rip and replace.
Project-based engagements for a full security pipeline typically range from $80,000 to $250,000, depending on the number of repositories, compliance requirements, and team size. Dedicated security teams start around $25,000 per month. Staff augmentation rates depend on seniority and specialization. We scope every engagement individually after an initial discovery call.
Basic pre-commit scanning can be operational within the first two weeks. A full enterprise pipeline with SAST, SCA, secret detection, and compliance automation takes 10 to 14 weeks. Smaller-scope engagements focused on a single team or repository can be fully deployed in 4 to 6 weeks.
Yes. Our engineering teams are based in Argentina, which shares time zones with the US East Coast. Real-time collaboration during your working hours, same-day responses, no 12-hour communication delays.
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