Secure AI infrastructure before it becomes business-critical risk.
AI systems introduce new security, privacy, and compliance challenges that traditional application and cloud security programs often do not fully cover.
Sensitive data may flow through prompts, retrieval pipelines, embeddings, vector databases, model endpoints, logs, and third-party APIs. Model outputs may create downstream security risk. Agents may call tools, access systems, or trigger workflows. Infrastructure may expose GPUs, data stores, model artifacts, and orchestration layers without the right controls. Compliance teams may need auditability, governance, and risk management before AI systems can be used in regulated environments.
CollTrixData helps organizations design and operate secure, compliant, production-ready AI infrastructure. We assess risks across the full AI stack, implement practical controls, and help teams build the security and governance foundation required for enterprise AI adoption.
AI security is not just cloud security with models attached.
Production AI systems combine application code, data pipelines, model-serving infrastructure, prompts, embeddings, vector stores, APIs, orchestration layers, user access, logs, and third-party dependencies. Each layer creates risk.
A secure AI platform must answer difficult questions:
Our Security & Compliance service helps organizations answer these questions and implement the controls needed to operate AI systems responsibly.
This service is designed for organizations building, scaling, or operating AI systems where security, privacy, compliance, or enterprise adoption matters. It is especially useful for teams that are:
We assess and strengthen the infrastructure used to run AI workloads. This may include cloud environments, Kubernetes clusters, GPU nodes, model-serving endpoints, Ray and KubeRay deployments, vLLM services, networking, storage, CI/CD pipelines, secrets, container images, and infrastructure-as-code.
The goal is to ensure AI infrastructure follows strong security practices before it supports critical workloads.
Model-serving endpoints can become high-risk interfaces if they are not properly controlled. We review authentication, authorization, network exposure, rate limits, request validation, endpoint isolation, abuse prevention, logging, model artifact handling, and access to inference services.
For LLM systems, we also evaluate risks such as prompt injection, sensitive information disclosure, model denial of service, and unsafe downstream use of model outputs.
AI systems often move sensitive data through places organizations do not fully track. We assess how data flows through prompts, context windows, embeddings, retrieval systems, logs, model APIs, storage layers, and downstream applications. This includes reviewing data classification, data minimization, encryption, retention, redaction, access control, audit logging, and boundaries between internal, customer, and third-party systems.
The goal is to reduce the risk of exposing sensitive or regulated data through AI workflows.
Retrieval systems introduce their own security and governance concerns. We review document ingestion, parsing, chunking, embedding generation, vector storage, metadata filtering, index access, tenant isolation, retrieval authorization, document-level permissions, and output grounding.
The objective is to ensure users only retrieve information they are authorized to access and that retrieval behavior can be monitored and governed.
AI systems need clear access boundaries. We evaluate IAM, role-based access control, service accounts, secrets management, environment separation, least privilege, privileged access, API keys, model endpoint access, vector database access, and administrative controls.
The goal is to prevent broad, unmanaged access to sensitive AI systems and data.
Agentic AI workflows create additional risk because models may call tools, query systems, write data, or trigger business processes. We help design controls for tool permissions, action approval, sandboxing, workflow boundaries, audit logs, human-in-the-loop review, input validation, output validation, and escalation paths.
The goal is to prevent AI agents from becoming uncontrolled automation layers.
AI infrastructure depends on models, containers, packages, datasets, pipelines, and third-party services. We assess supply-chain risk across model artifacts, container images, dependencies, CI/CD workflows, infrastructure modules, data sources, and external APIs. This includes reviewing image scanning, dependency management, artifact provenance, deployment approvals, environment separation, and rollback practices.
Security and compliance require evidence. We help establish logging, monitoring, audit trails, alerting, incident response workflows, security dashboards, access logs, model usage logs, data access records, and compliance evidence collection.
The goal is to ensure the organization can detect, investigate, and explain security-relevant AI activity.
We help teams prepare AI infrastructure for security, risk, and compliance review. This may include mapping controls to internal policies, SOC 2 expectations, HIPAA considerations, financial-services controls, data-governance requirements, privacy obligations, vendor-risk review, or AI risk-management frameworks.
We do not replace legal or compliance counsel. We provide the technical controls, documentation, and operating evidence needed to support compliance conversations.
A structured review of AI infrastructure, data flows, model-serving systems, access controls, deployment practices, and operational security posture.
A prioritized list of security, privacy, compliance, operational, and AI-specific risks with severity, impact, likelihood, and recommended remediation.
A clear view of how sensitive data moves through prompts, retrieval systems, embeddings, model endpoints, logs, APIs, storage layers, and downstream services.
A review of users, roles, service accounts, API keys, permissions, administrative access, and least-privilege gaps across the AI environment.
An assessment of document-level permissions, vector database access, metadata filtering, tenant isolation, retrieval authorization, and sensitive information exposure risk.
Specific recommendations for securing inference endpoints, model APIs, request validation, rate limits, network exposure, abuse prevention, and output handling.
For teams building AI agents, we define guardrails for tool access, action approval, logging, human review, policy enforcement, and escalation.
Technical documentation, control mapping, evidence requirements, and remediation priorities to support internal security, risk, legal, or compliance review.
A prioritized roadmap showing what to fix immediately, what to strengthen next, and what to institutionalize over time.
A leadership-ready summary of current AI security posture, key risks, required investments, and recommended next steps.
We begin by identifying the AI systems, workloads, users, data sources, models, infrastructure, third-party services, and business processes in scope.
The goal is to understand where AI is operating and what risk it introduces.
We map how data enters, moves through, and exits the AI system. This includes prompts, files, embeddings, context retrieval, model outputs, logs, storage systems, APIs, user actions, and downstream integrations.
The goal is to identify sensitive data exposure points and trust boundaries.
We review the security controls protecting the infrastructure and serving layer. This includes cloud security, Kubernetes controls, network exposure, secrets management, endpoint security, model artifact handling, CI/CD, container security, IAM, logging, and environment separation.
We assess risks specific to AI and LLM systems. This may include prompt injection, insecure output handling, sensitive information disclosure, model denial of service, unsafe tool use, retrieval leakage, supply-chain vulnerabilities, excessive agency, and weak auditability.
We organize findings by severity, business impact, implementation effort, and compliance urgency.
This gives engineering, security, and leadership teams a clear path for reducing risk without overwhelming delivery teams.
We define the technical controls, operational processes, documentation, ownership model, and evidence needed to strengthen AI security and compliance posture over time.
The goal is not a one-time checklist. The goal is a sustainable operating model for secure AI adoption.
After the engagement, your team will have:
Security & Compliance can be delivered as a focused AI security assessment, a compliance-readiness engagement, or part of a broader AI infrastructure modernization program.
It is commonly used before production launch, before enterprise customer rollout, after internal security review identifies gaps, or when AI systems begin handling sensitive or regulated data.
The engagement is designed to reduce risk while preserving engineering velocity.
CollTrixData understands that securing AI systems requires more than traditional cloud security.
AI infrastructure introduces new risk surfaces across prompts, embeddings, retrieval, model endpoints, GPU infrastructure, orchestration layers, agentic workflows, logs, and data pipelines.
We bring together AI infrastructure expertise, distributed systems knowledge, Kubernetes and cloud operations, model-serving experience, data pipeline understanding, and security-first engineering practices.
Our goal is to help teams build AI systems that are not only powerful, but secure, governed, auditable, and ready for enterprise use.