How Tamnoon is tackling the cloud security backlog with autonomous remediation

2026-06-16 · Source: Insight Partners · Field: Technology & Digital — Cybersecurity & Data Privacy, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

Tamnoon, a cloud remediation solution, addresses the critical backlog of security alerts generated by advanced detection tools but often left unresolved in live production environments. Founded by Dome9 veterans Marina Segal, Idan Perez, and Zohar Alon, Tamnoon's platform integrates with existing security tools to autonomously investigate, prioritize, and resolve issues. Its AI Agent, Tami, trained on real-life production executions, handles large-scale data processing and triage, while human experts validate decisions for higher-risk scenarios. The company launched in 2023 with a \$5.1M seed round, secured a \$12M Series A in September 2024, and expanded its customer base by over 300%. In 2025, Tamnoon processed 6.3 million alerts, resolving 2.7 million and protecting over 200,000 critical assets. It currently operates at level four autonomy, aiming for level five to enable direct machine execution of remediation actions.

Key takeaway

For MLOps Engineers or Directors of AI/ML struggling with cloud security alert backlogs, your current detection tools are likely insufficient for safe, timely remediation. You should evaluate autonomous remediation platforms like Tamnoon that integrate AI-driven fixing with human oversight for critical production changes. This approach can significantly reduce your operational burden, accelerate risk mitigation, and ensure your revenue-generating systems remain secure without manual intervention. Prioritize solutions that offer preemptive capabilities to stop misconfigurations before deployment.

Key insights

Autonomous cloud security remediation, combining AI and human oversight, effectively addresses the backlog of unresolved production alerts.

Principles

• Cloud security remediation is primarily an operational and process challenge, not solely a detection problem.
• Effective remediation requires knowing precisely when human intervention is necessary versus full automation.
• Moving into preemptive security, addressing misconfigurations pre-deployment, significantly reduces incident volume.

Method

Tamnoon's hybrid AI Agent, Tami, integrates with existing security tools to ingest alerts, then autonomously investigates, prioritizes, and resolves them, escalating high-risk cases for human validation.

In practice

• Train AI agents on real-life production executions for context-aware prioritization of cloud security issues.
• Establish a "confidence factor" to determine safety before applying fixes, especially considering system dependencies.

Topics

• Cloud Security
• Autonomous Remediation
• AI Agents
• Cloud Security Posture Management
• Production Security
• Misconfiguration Prevention

Best for: Investor, CTO, VP of Engineering/Data, AI Security Engineer, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Insight Partners.