Jeevan Badigari, Associate Director of Cybersecurity at Arada, discusses how the real estate development company is harnessing AI-driven security validation to stay ahead of emerging threats and support business growth securely.

Arada has adopted AI-driven penetration testing as part of its cybersecurity strategy. What were the key factors that led you to explore this approach, and how has it changed the way your team identifies and prioritises vulnerabilities?

As Arada continues to expand across multiple sectors beyond real estate, including hospitality, wellness, retail and digital customer experiences, our technology landscape has grown significantly. New applications, APIs, mobile platforms, cloud services and customer-facing digital services are being introduced at a much faster pace than traditional security assessment cycles can accommodate.

One of the biggest challenges for security teams today is reducing the time between the introduction of a vulnerability and its identification. Waiting for periodic penetration tests every few months is no longer sufficient when business platforms evolve continuously.

We therefore explored AI-driven security validation to complement traditional penetration testing. The objective was not to replace penetration testers, but to gain continuous visibility into an expanding attack surface and identify potential weaknesses much earlier.

A key part of our success has been integrating AI into our existing security and development workflows rather than treating it as a standalone security tool. We developed an AI-assisted security validation framework that combines enterprisegrade testing capabilities with large language models operating within controlled environments. The framework is provided with structured context about application architecture, user journeys, business workflows, API interactions and expected system behaviour, enabling the AI to reason beyond simple vulnerability signatures and assess security weaknesses within real business processes.

The system assists with reconnaissance, attack-path analysis, issue reproduction, evidence collection and remediation validation. It can also generate remediation guidance that developers can review through established engineering workflows. To improve accuracy, findings are validated against predefined business logic and testing scenarios before being escalated.

The results have been significant. Activities that previously required days, and sometimes weeks, to assess, validate and document can now be completed within hours. Over the last ten months, we have reduced false positives by approximately 78 % and saved more than 650 developer hours through automated remediation support and streamlined validation processes.

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