Vorlon Blog

Deloitte Features Vorlon's GenAI-Enabled Security Solution

Written by Netta Drimer | Aug 28, 2025

When I saw Vorlon listed in Deloitte's Cyber Report 2025: Israel’s AI-Cyber Frontier under "GenAI-Enabled Security Solutions," I was genuinely excited. Not just because external recognition feels good, but because the report captures exactly what I've been seeing in customer conversations for months: AI security isn't a future problem anymore. It's happening right now, in production, with real business impact.

The report places us in the "Autonomous Threat Detection & Response" category, and that feels right. Security teams are dealing with AI-generated activity they can't see, let alone control. Every copilot, every automation, every "smart" integration is making decisions with real data in real time. And most security tools are still thinking in terms of human users clicking buttons.

 

The dual AI imperative

CISOs, SecOps teams, and compliance folks are all grappling with what Deloitte's report captures perfectly: the need for both "AI for Security" and "Securing AI."

On the "AI for Security" side, teams want to use AI to accelerate threat detection and response across their complex SaaS ecosystems. They're tired of manually correlating alerts across dozens of tools and want AI-driven analytics that can spot anomalies in user behavior, API activity, and data flows faster than any human analyst.

On the "Securing AI" side, they need to govern what their AI tools are actually doing, i.e. which SaaS apps they're connecting to, what data they're accessing, and how they're behaving over time. 

The breakthrough insight for us was realizing these aren't separate problems. They're two sides of the same coin. You can't effectively secure AI without understanding the SaaS ecosystem it operates in. And you can't use AI for security without the comprehensive data flows and behavioral context that come from monitoring both human and machine activity across that ecosystem.

That's the fundamental problem we're solving: unifying AI-powered security capabilities with AI governance in a single platform. 

 

Our approach to SaaS ecosystem security

This is why I'm excited about what we're building at Vorlon. We're trying to make AI adoption visible and governable. Our approach is pretty straightforward: Map every connection, monitor every behavior, and make the risky stuff actionable.

Here's what that looks like:

  • DataMatrix: A live blueprint of your entire SaaS and AI ecosystem. Every app, every API call, every data flow, mapped in real time
  • Shadow AI Discovery: Find the AI tools your teams are using, whether they're approved or not, and show exactly what data they're touching
  • Sensitive Data Mapping: Trace PHI/PII and other sensitive classes across apps, agents, and third-party integrations
  • Behavioral Analytics: Baseline normal activity for both humans and machines, so when something changes, you know about it immediately
  • MCP Server: Query your SaaS ecosystem in plain language, "Show me all AI tools accessing customer data," and take actions like revoking access or rotating credentials
  • Automated Response: Execute guided or automated remediations with approvals and complete audit trails

The real challenge isn't technical

The hardest part of building AI security isn't the technology. It's the organizational dynamics. AI adoption is happening at business speed, not security speed. Teams aren't asking IT for permission to try a new AI tool; they're just signing up and connecting their data.

That's actually fine. Innovation should be fast. But security needs to be faster. When a new AI integration appears in your environment, you should know about it before the next security review meeting. When that integration starts behaving differently, you should have options beyond "shut it down" or "hope for the best."

This is what Deloitte's report gets right about the market. We need both AI for security and security for AI. Use AI to detect anomalies faster, yes. But also govern what your AI can access and how it behaves. Both problems require the same foundation: Complete visibility into your SaaS ecosystem.

 

What this means for you

If you're building security products, or if you're responsible for securing AI adoption at your company, here's my take on what matters:

  1. First, stop thinking about AI as a separate security domain. Your AI tools live in your SaaS stack. They use the same APIs, the same OAuth tokens, the same data stores. Secure them the same way.
  2. Second, focus on data flows, not just access controls. Traditional security asks "Who can access what?" The AI security question is "What is actually happening with our data?" Those are different problems requiring different approaches.
  3. Third, build for continuous monitoring, not point-in-time assessments. AI behavior changes as models update, as integrations evolve, as business needs shift. Your security posture needs to adapt in real time.

Where we go from here

Deloitte's recognition validates that we're working on the right problem at the right time. More importantly, every CISO who reads this report and thinks "Yeah, we need to figure out our AI security story" is someone we can help.

The future of SaaS security isn't about preventing AI adoption. It's about making AI adoption safe, visible, and governable. That's the problem we're solving, one data flow at a time.

Want to see how this works in practice? Let's talk!

 

About the author


Netta Drimer
is Head of Product at Vorlon, where she leads product strategy for the company's innovative SaaS ecosystem security platform. With nearly three years at Vorlon, Netta has been instrumental in developing solutions that give security teams the visibility and context they need to protect complex, interconnected SaaS environments.

Netta brings over a decade of experience spanning product management and software engineering across enterprise and consumer technology sectors. She holds a Bachelor of Science in Computer Science from The Interdisciplinary Center in Israel. Her unique combination of technical engineering background and product leadership experience allows her to bridge the gap between complex cybersecurity challenges and practical, user-focused solutions.