Zero-Trust AI Architecture
Identity-centric, least-privilege architectures for AI workloads — across model access, retrieval, and tool execution.
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AI Infrastructure & Cybersecurity
Secure, Scalable Foundations for Production AI
Architecture, deployment, MLOps, observability, and cybersecurity for organizations running real AI workloads. We design infrastructure that holds up under regulatory scrutiny and production load.
Consulting Practice
Six Areas We Cover
Each engagement is tailored to your stack and risk profile. We partner with internal engineering, security, and platform teams.
GPU Infrastructure
Cloud, on-prem, and hybrid GPU strategy. Capacity planning, scheduler design, multi-tenancy, and cost optimization for training and inference.
Book Consulting SessionMLOps & Secure Deployment
CI/CD for models and agents, container scanning, signed artifacts, environment promotion, rollback, and progressive delivery.
Book Consulting SessionObservability & Audit
Telemetry for agent decisions, retrieval citations, tool calls, costs, and latencies. Immutable audit trails for governance and incident review.
Book Consulting SessionPrivacy & Regulatory Guardrails
PII detection and redaction, data residency controls, prompt and output filters, and policy enforcement at the gateway layer.
Book Consulting SessionCyber Risk & AI Red Teaming
Threat modeling for AI systems, prompt-injection hardening, jailbreak resistance, supply-chain checks, and adversarial test campaigns.
Book Consulting SessionThreat Surveillance · Live
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Threat Detection
AI-Specific Cybersecurity in Action
We instrument your AI stack with the same rigor used for production financial systems. Every prompt, retrieval, and tool call is observable, scope-checked, and audit-logged. When something looks wrong, you see it within seconds — and so does our alerting pipeline.
- Prompt-injection and jailbreak detection at the gateway
- Data-exfiltration monitoring across retrieval and outputs
- Over-privileged tool-use alerts with auto-rollback
- Continuous adversarial test suites in CI
Architecture
Reference Architectures That Hold Up
We design AI platforms that survive contact with security review, audit, and real production traffic.
- Identity-aware gateways for model and tool access
- Network segmentation and egress controls for retrieval
- Secret management, key rotation, and credential isolation
- Multi-tenant isolation patterns for shared GPU clusters
- Documented threat models and control mappings (SOC2 / ISO / industry-specific)
- Bring-your-own-cloud and on-prem deployment options
Operations
MLOps & Observability
Production AI demands the same discipline as any other critical system, with extra signals unique to ML and agents.
- Versioned models, prompts, evals, and retrieval indexes
- Continuous evaluation and regression detection
- Cost, latency, and quality dashboards per workload
- Signed artifacts and reproducible build pipelines
- Incident response runbooks tuned for LLM/agent failures
- Capacity planning and auto-scaling for inference traffic
Cybersecurity
AI-Specific Cybersecurity
Traditional security tooling misses AI-specific failure modes. We assess and harden the parts that matter.
- Prompt-injection and jailbreak resistance testing
- Tool-use abuse and over-privileged agent detection
- Data-leakage analysis across retrieval and outputs
- Model and supply-chain provenance reviews
- Adversarial red-team campaigns with reproducible test sets
- Continuous evaluation pipelines for safety regressions
Plan an infrastructure or security review
Book a session with our consulting team. We'll scope the right engagement — architecture review, MLOps build-out, or AI security assessment.
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