All TechAble Secure engagements are led by AI security specialists with deep technical expertise in machine learning systems, adversarial AI research, and enterprise security architecture.
Comprehensive evaluation of an organization's AI systems, models, and infrastructure to identify vulnerabilities, governance gaps, and compliance risks before they are exploited.
Assessment Covers
Design and implementation of AI governance frameworks aligned with NIST AI RMF, EU AI Act, ISO/IEC 42001, and sector-specific regulatory requirements.
Frameworks We Use
Security review and design ensuring AI systems are built with security controls, least-privilege access, monitoring, and resilience from the ground up.
Architecture Scope
Structured programs preparing organizations for AI regulatory requirements and internal risk management mandates — documentation, controls, and audit readiness.
Adversarial testing using real-world techniques — prompt injection, jailbreaking, model inversion, indirect injection, and multi-step agent exploitation — to validate defenses before attackers do.
Attack Techniques Covered
Security review of infrastructure supporting AI systems — model serving environments, vector databases, API gateways, training clusters, and cloud AI services.
Where AI security meets enterprise infrastructure — TechAble Secure designs security architectures that are built for AI-native environments from the ground up, not bolted on afterward. Comprehensive design and review of enterprise security architectures for organizations operating AI systems, cloud-native infrastructure, and hybrid environments.
5-Phase Delivery
Document existing architecture, identify gaps, establish baseline
Develop target state with design documentation and control specs
Phased implementation roadmap with business case
Structured review with technical teams and executives
Architecture governance and change impact assessment
Zero Trust is not a product — it is a security philosophy and architectural discipline. TechAble Secure designs and implements Zero Trust frameworks purpose-built for organizations deploying AI systems, where traditional perimeter-based trust models are fundamentally inadequate. Based on NIST SP 800-207 and CISA Zero Trust Maturity Model.
| ZT Pillar | What We Assess | AI-Specific Layer | Target Outcome |
|---|---|---|---|
| Identity | IAM maturity, MFA, privileged access | AI service accounts, model API identities | Every identity verified, least privilege enforced |
| Devices | Endpoint visibility, device trust, MDM/EDR | AI workstation security, GPU node trust | All devices assessed, continuous compliance |
| Networks | Segmentation depth, lateral movement controls | AI cluster isolation, vector DB network controls | Micro-segmented, no implicit trust |
| Applications | App access controls, API gateway security | LLM API authorization, agent tool-use controls | Per-application policy, zero standing access |
| Data | Data classification, DLP, encryption | Training data access, model output classification | Always authorized and logged |
Regulatory Alignment
AI systems place unique demands on network infrastructure — from the high-bandwidth, low-latency requirements of GPU clusters and distributed training to the stringent isolation and monitoring requirements of production AI inference environments. TechAble Secure designs networks that are purpose-built for AI-era security and performance.
Distributed training across GPU clusters can generate hundreds of gigabits per second of east-west traffic — requiring purpose-built network fabrics. Model inference serving at scale requires consistent sub-10ms latency. TechAble Secure designs networks that address both requirements simultaneously.
Three Engagement Models
Current state documentation, performance benchmarking, security gap analysis, AI readiness evaluation, optimization recommendations
Requirements gathering, architecture design, detailed documentation, vendor selection, deployment planning, implementation oversight
Architecture governance, design review participation, change impact assessment, performance monitoring, technology roadmap advisory
Securing AI systems begins with the infrastructure they run on. TechAble Secure extends advisory into network design, system integration, and technology procurement — purpose-built for AI-era requirements.
AI systems place unique and growing demands on network infrastructure — from the high-bandwidth, low-latency requirements of GPU training clusters to the stringent isolation requirements of production AI inference environments. TechAble Secure designs, plans, and oversees deployment of enterprise networks purpose-built for AI-era security and performance.
Three Engagement Models
Current state documentation, performance benchmarking, security gap analysis, AI readiness evaluation
Requirements gathering, architecture design, detailed documentation, vendor selection, deployment planning
Architecture governance, design review participation, change impact assessment, performance monitoring
Standards Applied
Deploying AI systems requires more than model selection — it demands coherent end-to-end architecture across data pipelines, APIs, orchestration layers, identity systems, and cloud infrastructure. TechAble Secure designs and validates integrated AI system architectures, ensuring security, interoperability, and operational resilience from design through deployment.
Key Integration Domains
OpenAI, Anthropic, Cohere, open-source LLM APIs
Pinecone, Weaviate, pgvector, ChromaDB
LangChain, LlamaIndex, custom agent frameworks
AWS Bedrock, Azure OpenAI, Google Vertex AI
Frameworks Applied
Securing AI systems begins with the physical and cloud infrastructure they run on. TechAble Secure advises on, specifies, and coordinates the procurement and deployment of computing and technology infrastructure purpose-built for AI workloads — ensuring that hardware selection, configuration, and vendor relationships align with security requirements from day one.
Infrastructure Categories
NVIDIA, AMD GPU clusters; training and inference nodes
High-speed NVMe, object storage, and vector DB storage tiers
AWS, Azure, GCP — compute, storage, AI-managed services
Edge AI deployment, IoT infrastructure, on-device AI
Standards Applied
SR 11-7 · OCC AI Guidance · DORA
FISMA · FedRAMP · NIST AI RMF · EO AI
HIPAA · FDA SaMD · Clinical AI Safety
LLM Products · AI Agents · Platform Security
Conducted a full AI attack surface mapping for a mid-market financial institution deploying an LLM-powered client advisory tool. Identified 4 critical prompt injection vectors and 3 governance gaps ahead of a regulatory review — with a prioritised remediation roadmap delivered within two weeks.
Designed a Zero Trust architecture and AI governance framework for a government contractor preparing for CMMC Level 2 certification. Delivered a phased implementation roadmap and security domain model aligned to NIST SP 800-207 and the NIST AI RMF.
Performed adversarial red team testing on an enterprise SaaS platform integrating AI agents with external tool access. Discovered and documented a multi-step privilege escalation chain via indirect prompt injection — enabling the engineering team to close the vulnerability before customer launch.
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