Cross-domain correlation
Network, cloud, application, code, external attack surface, dark web credentials, identity and vendor risk, all in one graph, all collected natively. Nothing needs reconciling across naming schemes.
Attack path analysis maps the steps from an attacker's foothold to something worth stealing. Kervo AI builds those chains from your real environment, ranked by which single fix breaks the most.
Vulnerability management grades findings individually. Attackers chain them. A breach-dump credential still works, that account reaches a wiki on a host beside a build server, and the build server's service account can write to production. Together, that is a route from the internet to your deployment pipeline.
Scored one at a time, that chain is a medium, a low, an informational and a design decision, all ranked below the CVSS 9.1 on an unreachable test box. Severity has no concept of sequence, and sequence is how breaches happen.
Kervo AI keeps a live graph of your environment. Nodes are assets, identities and data stores. Edges are the relationships that actually exist: reachability, IAM trust, credential validity, API dependency, vendor connectivity.
From every plausible entry point it searches for routes to anything you have marked critical, where each hop is a MITRE ATT&CK technique the environment permits. Then it works out which single fix breaks the most paths. One patch, IAM change or closed port often collapses six or seven chains.
Network, cloud, application, code, external attack surface, dark web credentials, identity and vendor risk, all in one graph, all collected natively. Nothing needs reconciling across naming schemes.
Every hop maps to a tactic and technique: Initial Access via T1190, Credential Access via T1555, Lateral Movement via T1021. Your team gets paths in the vocabulary it already uses.
Tag what matters and Kervo AI continuously evaluates reachability to each. Criticality can be set manually, inherited from cloud tags, or derived from connectivity and data classification.
Fixes are ordered by how many chains each collapses and what those chains reach. Whoever is patching gets a reason, which improves the odds of it happening this sprint.
When a path was first detected, how it changed, when it closed. Useful for audit, and for the conversation about whether risk is genuinely trending down.
Path construction runs continuously. As Know feeds in new assets and exposures, affected paths recalculate, so a chain opened by a widened security group on Tuesday afternoon is flagged on Tuesday afternoon.
Scoring weighs how exploitable each step is, how many steps the chain needs, and the value of what sits at the end. A two-hop path to your identity provider outranks a six-hop path to a staging database.
The example above involves four findings from four domains. In a typical stack that is four products and four sets of asset identifiers, and joining them reliably is where "unified" platforms quietly fail. False paths destroy trust faster than no paths. Kervo AI does not reconcile: all four findings were written to the same model against the same asset record.
It maps the steps an attacker would take from a foothold to a valuable target using the real relationships in your environment, and shows which single fix breaks the chain.
BAS runs simulated attacks to test detection. Attack path analysis maps the routes that exist without running anything against production. They complement each other.
External attack surface, cloud configuration, network scans, application and code findings, dark web credentials, identity relationships and vendor risk. Enterprise plans also ingest findings from tools you already run.
You tag them, or Kervo AI infers them from connectivity, data classification and cloud tags.
Continuously, as new vulnerabilities, configuration changes and assets flow into the graph.
We connect during the demo and walk the real chains, not a scripted example on sample data.