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    <title>DEV Community: Oluwole Ajayi</title>
    <description>The latest articles on DEV Community by Oluwole Ajayi (@oluwole_ajayi).</description>
    <link>https://dev.to/oluwole_ajayi</link>
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      <title>DEV Community: Oluwole Ajayi</title>
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      <title>VeriLync- Application Security for SaaS Scale-ups</title>
      <dc:creator>Oluwole Ajayi</dc:creator>
      <pubDate>Sun, 21 Jun 2026 19:25:39 +0000</pubDate>
      <link>https://dev.to/oluwole_ajayi/verilync-application-security-for-saas-scale-ups-3o0l</link>
      <guid>https://dev.to/oluwole_ajayi/verilync-application-security-for-saas-scale-ups-3o0l</guid>
      <description>&lt;p&gt;I studied MSc Applied Cybersecurity at the University of South Wales. My dissertation was titled "Developing and Evaluating an AI-Assisted SQL Injection Detection Framework Using ChatGPT and Machine Learning Techniques." The artefact combined ChatGPT with machine learning to detect SQL injection, return remediation guidance, and present best-practice educational material.&lt;/p&gt;

&lt;p&gt;As a proof of concept, it worked. But evaluation was part of the point, and evaluating it honestly surfaced three limitations I could not stop thinking about.&lt;/p&gt;

&lt;p&gt;First, detection alone is not the hard part. Telling someone they have a vulnerability is the easy part. Telling them what it means for their business, and exactly how to fix it in their stack, is the part that actually helps.&lt;/p&gt;

&lt;p&gt;Second, a general-purpose AI model is not trustworthy enough, on its own, for security decisions. It needs guardrails: deterministic detection first, AI for explanation only, with a fallback when it fails. Letting a model decide what counts as a vulnerability is the wrong architecture.&lt;/p&gt;

&lt;p&gt;Third, the people who need this most are not large enterprises with security teams. They are the 20-to-200-person SaaS companies that have a CTO, a board asking for SOC 2, and enterprise customers running security questionnaires, but no security engineer to make sense of any of it.&lt;/p&gt;

&lt;p&gt;I recorded these as future considerations at the end of the dissertation. One of them became VeriLync.&lt;/p&gt;

&lt;p&gt;VeriLync is a static application security platform for SaaS scale-ups. It analyses your source code, and every finding comes with two things: an executive summary for non-technical stakeholders, and a stack-specific remediation example for your developers. Findings are linked to relevant security controls commonly referenced in SOC 2, ISO 27001, GDPR, and Cyber Essentials Plus. The AI explains the findings; it does not decide them.&lt;/p&gt;

&lt;p&gt;The gap my dissertation pointed at is the gap VeriLync is built to close.&lt;/p&gt;

&lt;p&gt;VeriLync is in early access. If you run a SaaS company that has more code than security headcount, I would value having you on the waitlist.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.verilync.com" rel="noopener noreferrer"&gt;www.verilync.com&lt;/a&gt;&lt;/p&gt;

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      <category>security</category>
      <category>machinelearning</category>
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