AI-enabled cybersecurity solutions are everywhere. Each promises faster detection, smarter responses, and better outcomes. But how do you know if they really work?
According to Gartner, over 40% of AI projects will be canceled by 2027 because they don’t deliver results (Gartner, 2025). Enterprise cybersecurity leaders are feeling this pressure. Choosing the wrong AI-powered cybersecurity solution means wasted resources and potential security gaps.
A recent McKinsey report (Seizing the Agentic AI Advantage) highlights another critical issue: many organizations struggle because they don’t adequately test AI solutions against realistic scenarios. They found that companies using robust testing methods significantly improved their AI project success rates. Testing AI with actual threats before procurement helps organizations separate marketing promises from actual performance.
Here’s how it works:
- Use threat-based testing to test AI-enabled cybersecurity tools.
- Automate data collection and analysis see how well these solutions detect, block, and respond effectively.
- Get clear, measurable outcomes that show exactly how each solution performs.
This approach removes guesswork. You can automatically compare different AI-powered cybersecurity solutions using real performance data. You’ll quickly see which solutions protect your organization best and where you should invest your resources.
This clarity is critical right now, especially as the AI cybersecurity market becomes increasingly crowded and noisy. Simple, automated testing ensures your decisions are based on facts—not hype.
If you’d like help getting started, reach out. We’re here to ensure your AI cybersecurity investments deliver real value and help you avoid solutions that don’t.
