Four security surfaces, one correlated risk model
Each pillar stands on its own. Together they produce the context no single scanner can see — which is what makes a reachability verdict possible in the first place.
Find it before it merges — SAST, SCA, secrets and IaC
Static analysis, dependency intelligence, credential detection and infrastructure-as-code review run on every pull request, so the class of problem that is cheapest to fix is caught while the author still has the context.
- SAST across modern languages with data-flow and taint tracking
- Software composition analysis with transitive dependency mapping
- Secrets detection before merge, with a rotation path attached
- IaC scanning for Terraform, Kubernetes and container definitions
- Findings annotated in the PR, not in a separate console
Posture, entitlement drift and the paths that actually lead somewhere
Continuous inventory of accounts, resources and identities across AWS, Azure and GCP. Misconfiguration and privilege drift are ranked by what is genuinely reachable — from the internet, or from a role an attacker could already hold.
- CSPM across accounts, regions and resource types
- CIEM: entitlement mapping, privilege drift and unused permissions
- Internet exposure analysis down to the workload
- Blast-radius scoring for every misconfiguration
- Drift alerts routed to the team that owns the account
What is actually running, and what it is actually doing
Anomaly detection on live workloads closes the loop between build time and production. A CVE that is present in a repository but never deployed is noise; the same CVE running behind a public load balancer is an incident waiting to happen.
- Workload behaviour baselining and anomaly detection
- Build-time findings matched to running containers and hosts
- End-to-end attack paths from entry point to sensitive data
- Live confirmation that a fix actually removed the exposure
- Signals shared with Datadog, Splunk and your SIEM
Discover the models and agents nobody registered
AI systems are moving into production faster than any governance process around them. Securefase discovers models, agents and shadow AI across the estate, then assesses posture, data exposure and policy the same way it does any other workload.
- Shadow AI discovery across code, cloud and SaaS integrations
- Model and endpoint posture assessment
- Data-exposure checks on prompts, payloads and training sources
- Agent permission review — what can it reach, and change?
- Policy controls and approval gates for AI in production
One platform in place of five point tools
Free tier gives one repository and five scans a month, so evaluation runs on real code — not a sandbox.