CyberKord
CyberKord is an autonomous security operations platform that answers alerts from existing EDR, SIEM, and exposure tools without waiting for a human analyst. It builds a live map of identities, assets, and attack paths, then re-scores, simulates, and contains threats at machine speed—with every step logged and readable. The platform operates read-only until an organization grants it permission to act autonomously on specific alert types.
- Cybersecurity
- Artificial Intelligence
- Software Only
- AI Agents
- Enterprise Software
Funding
Raised to date
€2.7MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Est. $2.9Macross 1 round
€2.7M · Pre-Seed
Announced Nov 2026
Disclosed cumulative: $2.9M
Founders
Founder details are not available yet.
Product
Problem
Traditional security tools detect intrusions, but attackers increasingly move through networks using valid identities, including stolen human credentials or compromised AI agents. Firewalls, EDR, and IAM systems fail to flag legitimate logins that violate business intent, leaving organizations blind to insider threats, lateral movement, and access drift until after damage occurs. AI-driven attackers exploit vulnerabilities from the backlog at machine speed, often before patches exist.
Solution
CyberKord provides an intelligence layer that sits above an organization's existing security stack, correlating signals across identity providers, SIEMs, EDR/XDR tools, and cloud platforms to detect what no single system can see alone. The platform builds a behavioral baseline for every identity—human, machine, or AI agent—and flags actions that are technically authorized but contextually wrong. A proprietary graph foundation model identifies complex attack patterns across siloed data sources, while a continuously updated digital twin maps every identity, asset, and attack path to show the enterprise through an attacker's eyes. CyberKord operates autonomously: it detects, diagnoses, resolves, and documents incidents end to end, with every step validated and logged through existing tools. When a threat is detected, the platform can auto-respond by revoking access, quarantining sessions, or alerting teams, and it simulates change impact before deployment to prevent exposure.
Target Audience
Primary customers are enterprise security teams, CISOs, and IT operations leaders at organizations with complex identity ecosystems, including those deploying AI agents, who need to detect insider threats, remediate misconfigurations, and prevent unauthorized access that traditional perimeter-based tools miss.
Features
- Live digital twin providing a real-time map of every identity, asset, and attack path to critical systems
- Behavioral baselining per identity, role, and context to distinguish legitimate exceptions from real threats
- Intent detection that flags "valid but wrong" access, catching what IAM authorizes but business rules do not
- Graph foundation model that correlates signals across siloed data sources to identify complex attack patterns and reduce alert fatigue
- Just-in-time access aligned to business context with auto-revocation when role, project, or risk shifts
- AI agent guard that inventories every AI agent as an identity, simulates blast radius before deployment, and detects drift when intent diverges
- Fully autonomous incident resolution: detects, diagnoses, fixes, and documents issues end to end without human intervention, with changes simulated before going live
- Integration with Okta, Azure AD, Ping, Splunk, Sentinel, QRadar, CrowdStrike, SentinelOne, AWS, Azure, GCP, Jira, and ServiceNow