Secure the AI SDLC
End-to-end security for AI development lifecycles — from model training to deployment and monitoring.
AI introduces new attack surfaces across code, data, models, and runtime. GoSentrix is building a unified security layer that brings visibility, controls, and risk context to every stage of the AI SDLC.
Why securing AI is not the same as securing traditional software
AI systems introduce unique security challenges that require new approaches and tools.
Expanded attack surface
Models, training data, prompts, pipelines, and inference APIs all introduce new risk vectors.
Blended responsibilities
AI security spans developers, ML engineers, data teams, and security — without clear ownership today.
Dynamic behavior
AI systems evolve after deployment, making static security controls insufficient.
Regulatory pressure
AI governance, data provenance, and model accountability are becoming mandatory, not optional.
Coverage across the AI development lifecycle
GoSentrix provides security visibility and controls across every stage of AI development.
Design & Architecture
- Threat modeling for AI workflows
- Risk identification across data, models, and integrations
- Security requirements for AI use cases
Data & Training Pipelines
- Visibility into training data sources
- Data lineage and access controls
- Detection of insecure data handling and exposure risks
Model Development
- Secure model repositories and artifacts
- Dependency and supply-chain risk in ML tooling
- Governance over model versions and experimentation
Deployment & Runtime
- API and inference endpoint exposure
- Cloud, container, and identity risks
- Runtime behavior monitoring and anomaly detection
Monitoring & Governance
- Continuous risk assessment
- Auditability and traceability
- Alignment with emerging AI regulations and internal policies
Our approach
GoSentrix approaches AI security the same way we approach modern application security: by correlating signals across code, cloud, runtime, and data into a single risk-aware view.
Graph-native context
Understand how models, data, services, and infrastructure relate — and where risk propagates.
Control-aware risk prioritization
Evaluate risk based on exposure, environment, and existing security controls.
Explainability first
Every risk is traceable to evidence, relationships, and impact — not black-box scores.
Designed for security teams
Built for CISOs, AppSec, and platform security — not just ML engineers.
Coming soon
We're actively developing Secure the AI SDLC with input from security leaders, platform teams, and AI practitioners.
Early releases will focus on visibility, risk correlation, and governance — not one-off AI point solutions.
Interested in becoming a design partner? We'd love to talk.