Cogent Security Raises $42M to Automate Vulnerability Management with AI Agents, Bringing Total Funding to $53M

Cogent Security Raises $42M to Scale AI Agents for Vulnerability Remediation

Cogent Security announced a $42 million Series A led by Bain Capital Ventures, with participation from Greylock Partners, Definition, and executives from OpenAI, Abnormal Security, and Datadog. This brings the company's total funding to $53 million and will be used to accelerate product development.

The San Francisco-based company is building an agentic AI platform that automates the heavy lift in vulnerability management-investigation, prioritization, and remediation. It connects and normalizes data across environments, filters scanner noise, adds business context, and moves fixes through existing workflows. The goal is straightforward: shrink risk faster with less manual coordination.

"Security teams are drowning in coordination work: chasing down system owners, writing tickets, proving fixes happened. We built AI agents that handle that work end-to-end, so security teams can finally keep pace with attackers," said Vineet Edupuganti, CEO and co-founder of Cogent.

Why this matters for management

• Turn vulnerability backlog into a predictable, measured pipeline of fixes.
• Reduce coordination costs across security, IT, and dev teams without adding headcount.
• Improve MTTR and SLA adherence by automating ticketing, ownership routing, and verification.
• Prioritize what actually matters to the business, not just what has a high severity score.

How agentic AI can streamline your vulnerability operations

• Consolidates and normalizes scanner output, config data, and asset inventories to reduce noise.
• Prioritizes using business context and environmental signals, beyond CVSS alone.
• Surfaces emerging threats and aligns them to affected assets and owners.
• Generates remediation plans and pushes them into your existing ITSM/dev workflows.
• Closes the loop by tracking proof-of-fix and exceptions with audit-ready evidence.

Questions to ask your team and the vendor

• Data coverage: Which scanners, asset inventories, CMDBs, and ITSM tools are supported? How is data quality enforced?
• Prioritization: What signals (business criticality, exploit activity, exposure) drive ranking? How are ties and conflicts handled?
• Workflow control: What requires human approval vs. fully automated action? Can we set per-system guardrails and change windows?
• Evidence: How is remediation verified (config drift checks, rescans, log proof)? Is evidence exportable for audits?
• Security: How are credentials stored and scoped? Is there full audit logging, SSO, RBAC, and least-privilege access?
• Reliability: What are the SLAs, rollout safety checks, and rollback paths if an automated change causes issues?
• Deployment: SaaS vs. private deployment options? Data residency controls? Integration effort and typical time-to-value?

90-day pilot plan (practical and measurable)

• Days 0-30: Connect one scanner, one CMDB, and one ITSM project. Define approval policy. Baseline metrics: backlog size, MTTR, ticket cycle time, false-positive rate.
• Days 31-60: Enable automated ticket creation and owner routing for a narrow scope (e.g., one business unit). Track proof-of-fix capture and exception handling.
• Days 61-90: Expand scope and introduce limited auto-remediation where safe. Compare results to baseline; decide on scale-up criteria and budget.

Metrics that prove value

• Backlog reduction (%) and time to burn down criticals.
• MTTR improvement (hours/days) by severity and asset class.
• Percent of tickets auto-created, correctly routed, and auto-closed with verified evidence.
• False-positive/duplicate reduction vs. prior quarter.
• Analyst hours saved per week and avoided headcount growth.
• SLA compliance rate and change-failure rate for remediations.

Risk and governance checklist

• RBAC with approval thresholds; segregation of duties for creation vs. approval of fixes.
• Comprehensive audit logs; evidence packages suitable for SOX/PCI/SOC 2.
• Data handling: residency options, encryption, and no persistence of sensitive payloads in model training.
• Guardrails: test in staging where possible; emergency stop and rollback paths; explicit change windows.
• Hallucination and action limits: require validation steps for destructive or high-impact changes.
• Third-party risk review and security assessment before broad rollout.

Budget and org impact

Expect savings from reduced coordination work, fewer reopens, and faster closure of high-risk items that usually stall in handoffs. Most teams reallocate analysts to higher-value engineering and threat work instead of adding headcount.

Model the ROI with conservative assumptions: ticket cycle time saved, percent of fixes verified without manual effort, and risk reduction for assets powering revenue or regulated workloads. Treat it as a platform investment-success depends on clean data, clear ownership, and well-defined approval rules.

Market signal

Backers like Bain Capital Ventures and Greylock leaning into agentic AI for vulnerability management signals growing confidence in automation beyond analysis-into action and verification. Useful for leaders tracking where security operations is heading and how to future-proof team structure and tooling.

Helpful references

• CVSS Overview (FIRST) - baseline severity context.
• CISA Known Exploited Vulnerabilities Catalog - prioritize issues under active exploitation.

For leaders setting AI-in-security strategy

If you're aligning AI initiatives with security governance, integration, and enterprise risk, see our AI Learning Path for CIOs for a structured approach to roadmapping, controls, and measurable outcomes.