Case Study: 10x Faster Implementation of Deepsec by AI Agents in a SME

Automating code security is a challenging topic for many Polish SMEs: there is a shortage of personnel, and a dedicated DevSecOps team can be a significant expense. This article illustrates how Deepsec and AI agents can enable effective security audits in just a few days, using a real example from a Polish tech company.

Challenge: Code Security Without DevSecOps

In Polish SMEs, code security often takes a back seat—not out of negligence, but due to limited resources. Development teams focus on features, while security testing is performed irregularly or superficially.

Hiring a dedicated DevSecOps specialist can cost anywhere from several thousand to over 20,000 PLN gross per month, depending on the region, experience level, and company size. For many SMEs, particularly those outside major cities or with limited IT budgets, this is a challenging expense. Consequently, many founders and CTOs seek solutions that do not require team expansion but allow for peace of mind.

The guiding question is: can code security auditing be automated without investing in a separate DevSecOps department?

Deepsec – Open-Source AI Agent for Code Auditing

Deepsec is a new open-source tool that utilizes AI agents (autonomous programs analyzing code based on prompts and learned patterns) to identify security vulnerabilities.

Unlike traditional SAST scanners, Deepsec operates in a more contextual manner: it can analyze the entire repository, identifying not only obvious vulnerabilities but also subtle gaps, such as those arising from business logic.

Thanks to its integration capabilities via webhooks, Deepsec can be seamlessly incorporated into any development workflow without the need to overhaul existing processes.

• Open-source, no licensing costs
• Integration with popular repositories (GitHub, GitLab)
• Configurable alerts and reports

Practice: Implementing Deepsec in a Polish SME

Company X (a tech firm with a 40-person team, providing SaaS for the service industry) faced an increasing number of security reports from clients. Previous manual code reviews did not guarantee the detection of all errors.

The implementation of Deepsec began with testing in a sandbox environment. After the initial configuration of the AI agents (setting prompts tailored to PHP + JS code) and integration via webhooks, the tool began automatically analyzing every pull request.

The result? The first complete code security audit took 40 minutes (previously it took 6-8 hours of manual work). Deepsec identified three significant vulnerabilities that had been missed in earlier tests.

Alerts from Deepsec were sent directly to the team's Slack channel—each developer was immediately informed of potential issues.

• Implementation time: 2 days
• Automated tests on every PR
• Integration without changes to workflow

Business Results and ROI: What Did the CTO Gain?

After one month of using Deepsec, the number of security reports from clients dropped by 60%. Code audits became regular and fully repeatable—without the need to hire additional personnel.

The team estimated a savings of 60 hours of developer work per month. The implementation cost amounted to approximately 8-10 hours of DevOps work, covering tool configuration, integration testing with the existing workflow, and training the development team on using Deepsec's alerts and reports. At market rates, this translates to an expense of around 2-3 thousand PLN one-time.

The key takeaway: automation through AI agents allowed developers to focus on product development, while the CTO gained concrete data regarding security improvements.

• 60% fewer security incidents
• 10x faster code audits
• ROI returned within 2 weeks

Conclusions and Challenges: What to Know Before Implementation?

Deepsec and AI agents are not a magic solution: they require a good setup, well-thought-out prompts, and integration testing. It is crucial to clearly define which parts of the code are critical.

It’s important to ensure clear reporting and integration of alerts with the team’s tools (Slack, Jira). The biggest pitfall is assuming that AI will detect all potential threats—in practice, even the best AI tools may overlook specific, complex vulnerabilities, such as those arising from unusual business logic, authorization errors, or interactions with external APIs. Therefore, AI auditing should complement the code review process conducted by experienced developers, rather than replace it.

In summary: even without an extensive DevSecOps team, Polish SMEs can achieve real improvements in security and save time.

• Test integration in a sandbox
• Establish clear alert criteria
• Consider AI as support, not a replacement

Automating code security audits with Deepsec and AI agents presents a real opportunity for Polish SMEs to enhance their security posture without significant investment. If you’d like to learn how to implement a similar solution in your organization, schedule a consultation, and let’s discuss your workflow.