The Automated Vulnerability Scanning with Agentic AI project leverages large language models to autonomously plan, execute, and report on security assessments. By integrating tools like Bandit for static analysis, Nmap for network enumeration, and CVE databases for vulnerability matching, the agent creates a step-by-step scanning workflow. It analyzes code repositories for insecure patterns, probes network ports for exposed services, correlates findings with known vulnerabilities, and generates an actionable report with risk ratings and remediation guidance. Users can customize scanning pipelines, define target scopes, and integrate results into existing CI/CD pipelines. This LLM-driven agentic framework reduces manual effort and accelerates the discovery and mitigation of security risks across applications and infrastructure.

Ostorlab leverages machine learning and automated scanning engines to perform end-to-end mobile app security assessments. Developers upload app binaries or connect repositories, and Ostorlab’s AI performs static code analysis, dynamic runtime testing, and network traffic inspection. The platform highlights critical, high, and medium-risk issues, offers remediation guidance, and integrates into development workflows for continuous monitoring and compliance management.

Klavis.ai is an enterprise-grade AI observability agent that unifies logs, metrics, traces, and events into a single AI-driven layer. It supports connectors for Prometheus, Elastic, Grafana, AWS CloudWatch and more. Teams can ask natural-language questions about system health, receive instant anomaly alerts, and get guided remediation steps. Its AI models correlate data across services to pinpoint failures, reduce alert noise, and proactively surface performance issues before they impact users.