Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I am a seasoned product manager with over 15 years of experience in the digital and ICT industries. Holding a bachelor’s degree in computer engineering, and a certified Ethical Hacker.

As the Cybersecurity product lead, I developed and launched (0-1) enterprise-grade cybersecurity products at geographically dispersed locations from South Asia to the Middle East to North America. 

Q2. What are the estimated global and regional market sizes for AI-driven cybersecurity solutions, and which segments are experiencing the fastest adoption?

General Cybersecurity Market Size (AI-Inclusive) valued at USD 301 B in 2025, with a CAGR of 12.6%, is expected to reach 878 B by 2034 as per a study from Yahoo. 

Roughly 12% of the global AI cybersecurity market (USD 36 B) is expected to reach USD 229 B, with a CAGR of 25.8% (more than double the overall cybersecurity growth rate), according to separate research by Global Growth Insights.

It is also in alignment with the overall cybersecurity market adoption, led 70% by large organizations and 30% by SME.

Q3. How do adoption rates of AI-driven cybersecurity solutions differ across key industry verticals, and what are the unique factors hindering or accelerating uptake in each segment?

With Cloud and SASE adoption, sectors like BFSI, Government, Defense, healthcare, and Retail E-commerce are the main pillars in terms of vertical adoption. They have higher exposure to threat as a broader and complex consumer-facing portfolio that is also required to comply with tons of regulatory and policy requirements. 

Q4. Which recent advances in AI or machine learning have most effectively improved threat detection and real-time incident response rates within network security platforms?

AI is playing a pivotal role in identifying and mitigating threats in real-time with ML and behavior analysis.

For threat detection, Deep learning for anomaly detection, Behavioral analysis models, Encrypted traffic analysis, and Federated learning are key tactics being used by advanced AI. 

For real-time response, Automated containment and self-healing network, Edge AI, AI-powered SOC, and Predictive Threat Intelligence are being used. 

Q5. Who are the top innovators and fastest-growing platforms in AI-driven cybersecurity, and what features or business models set them apart from legacy providers?

Depending on the product, market leaders vary; however, predominant and most progressive products in cybersecurity are being delivered by CrowdStrike (EDR, Cloud), Palo Alto (Network and Cloud), SentinelOne (XDR), Darktrace, Okta, etc 

Q6. What is the long-term outlook for AI-driven automation in threat intelligence—are we moving toward fully autonomous security operations, and what implications does this have for the industry and talent needs?

Agentic AI is emerging as a game-changer, enabling intelligent agents to autonomously triage alerts, investigate threats, and even execute response actions.

Autonomous SOCs are already reducing alert fatigue, improving detection speed, and cutting breach costs by millions.

Levels 1 and 2 are being handled by LLM-based Agentic AI; however, L3 is still required to measure the depth of an issue based on the customer's business needs and add sensitivity.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

• I would definitely ask a few questions around:
• What is the adaptability model?
• How prepared are they to AI-based false positive responses (AI can be fooled or poisoned)?
• Strategy to maintain integrity, sensitivity, and accuracy
• How are they going to maintain the edge over competition without impacting customer experience?