AI is being used on both sides of the security equation - attackers are using it to craft more convincing phishing and exploit code faster, while defenders are using it to detect anomalies, surface vulnerabilities, and automate threat response. This category covers tools that use AI to protect systems, audit code, and maintain privacy compliance.
Detection accuracy
False positives are the enemy of security tools - teams that get flooded with noise stop paying attention. Look for published benchmark results and evaluate against your actual environment.
Integration with your stack
A security tool that requires its own dashboard, separate from your existing workflow, tends to get ignored. Look for SIEM integrations, Slack notifications, and API access.
Compliance coverage
If you need to meet SOC 2, GDPR, HIPAA, or ISO 27001 requirements, check that the tool produces the audit evidence and reports those frameworks require.
Response time and support
Security incidents happen outside business hours. Check SLA guarantees and whether human support is available for critical alerts before you need it.
AI security tools can identify vulnerabilities, but they don't replace human security assessors for formal audits. They reduce the surface area a human auditor has to cover and catch issues earlier in the development cycle.
Prompt injection - where malicious instructions are hidden in user inputs or documents that AI systems process - is currently one of the most underappreciated risks. Phishing amplification (AI-generated targeted attacks at scale) is another. Traditional perimeter defenses weren't designed for either.
It can be. Any AI tool that processes personal or health data on your behalf is a data processor under GDPR, requiring a Data Processing Agreement. For HIPAA, you need a Business Associate Agreement. Always check before feeding sensitive data to any AI tool.
