Secure AI Adoption
Implement artificial intelligence securely while maintaining data privacy and regulatory compliance.
AI is only as trustworthy as the security built around it.
Generative AI and large language models are reshaping enterprise operations — but every model you deploy, every API you expose, and every dataset you feed into an AI pipeline introduces new attack surfaces that traditional security frameworks were never designed to address. Prompt injection, model inversion, training data poisoning, and AI supply-chain compromise are not theoretical risks. They are active threats targeting organizations today.
Vimix's Secure AI Adoption practice embeds security, privacy, and governance into every layer of your AI lifecycle — from model selection and data pipeline design through to deployment, monitoring, and regulatory compliance. We help you move fast with AI without exposing your organization to the risks that come with moving carelessly.
Our Research
Of enterprise AI deployments have at least one critical security vulnerability at the time of initial deployment — according to Gartner AI Security Survey 2024.
Higher likelihood of a material AI-related security incident for organizations without a dedicated AI security program versus those with structured controls in place.
Year by which the EU AI Act's high-risk AI system requirements become fully enforceable — organizations without compliance programs face fines of up to €30M or 6% of global turnover.
Our Services
We deliver end-to-end AI security capabilities — spanning risk assessment, architecture hardening, governance frameworks, and continuous monitoring — so your AI investments are protected from the inside out.
AI Risk Assessment & Threat Modelling
Before you deploy, we assess. Our AI risk assessments evaluate your models, pipelines, and integrations against the OWASP Top 10 for LLMs, MITRE ATLAS, and NIST AI RMF — identifying vulnerabilities including prompt injection, insecure output handling, model denial-of-service, and training data exposure. Every finding is risk-scored and mapped to a remediation roadmap.
Secure AI Architecture & Pipeline Design
We architect AI systems with security as a first principle — not an afterthought. This includes input validation and sanitization layers, output filtering and content moderation controls, API gateway hardening, secrets management for model credentials, and network segmentation for AI inference workloads across AWS, Azure, and GCP.
AI Governance & Responsible AI Framework
We help you build the governance structures that regulators, boards, and enterprise customers increasingly demand. This includes AI policy development, model cards and transparency documentation, bias and fairness testing, explainability frameworks, and alignment with the EU AI Act, NIST AI RMF, and ISO/IEC 42001 — the international standard for AI management systems.
Data Privacy & Training Data Security
The data you train on defines the security posture of your model. We implement differential privacy techniques, data anonymization and pseudonymization pipelines, access controls for training datasets, and data lineage tracking — ensuring your models are not inadvertently trained on sensitive PII, regulated data, or proprietary information that could be extracted through adversarial queries.
LLM & GenAI Security Testing
We conduct adversarial testing of your LLM deployments using red-team techniques specifically designed for generative AI: prompt injection attacks, jailbreak attempts, indirect prompt injection via RAG pipelines, model inversion probing, and membership inference attacks. Our findings go beyond theoretical — we demonstrate exploitability and provide validated mitigations.
AI Security Monitoring & Incident Response
AI systems require continuous monitoring for anomalous behaviour that traditional SIEM tools miss. We deploy AI-specific monitoring covering prompt anomaly detection, output drift analysis, unusual query pattern alerting, and model API abuse detection — integrated with your existing security stack (Splunk, Microsoft Sentinel, Datadog) and backed by a defined AI incident response playbook.
Our Approach
Our Secure AI Adoption methodology follows a structured lifecycle that addresses security at every phase — from initial AI strategy through to production deployment and continuous governance.
Assess
AI security posture assessment, threat modelling against OWASP LLM Top 10 and MITRE ATLAS, regulatory gap analysis (EU AI Act, NIST AI RMF, ISO 42001), and a risk-prioritized remediation roadmap.
Architect
Secure-by-design AI architecture: input/output guardrails, API security hardening, secrets management, network segmentation, and privacy-preserving data pipeline design.
Govern
AI governance framework development: policies, model cards, bias testing, explainability documentation, and board-ready AI risk reporting aligned to regulatory requirements.
Test
Adversarial red-teaming of LLM and GenAI deployments: prompt injection, jailbreak testing, RAG pipeline security, model inversion probing, and supply-chain integrity validation.
Monitor
Continuous AI security monitoring: prompt anomaly detection, output drift alerting, API abuse detection, and AI-specific incident response playbook activation.
What we cover
Comprehensive
Security Capabilities
Security controls purpose-built for the AI attack surface.
Prompt Injection & Jailbreak Defence
Prompt injection is the most exploited vulnerability in LLM deployments today — attackers craft inputs that override system instructions, exfiltrate context, or manipulate model behaviour. We implement multi-layer defences: input validation and sanitization, system prompt hardening, instruction hierarchy enforcement, and output filtering — validated through continuous adversarial red-teaming against your specific model and deployment context. Our controls are tested against direct injection, indirect injection via RAG retrieval, and multi-turn manipulation chains.
Training Data Security & Privacy
Differential privacy, data anonymization pipelines, and access controls to prevent sensitive data exposure through model outputs.
Model Supply Chain Integrity
Verification of third-party model provenance, dependency scanning, and integrity checks to prevent poisoned model adoption.
AI API & Integration Security
API gateway hardening, rate limiting, authentication enforcement, and secrets management for all LLM API integrations.
AI Governance & Compliance
EU AI Act, NIST AI RMF, and ISO/IEC 42001 alignment — with audit-ready documentation, model cards, and board-level reporting.
Continuous AI Monitoring
Real-time anomaly detection for prompt patterns, output drift, and API abuse — integrated with Splunk, Sentinel, and Datadog.
Adversarial Red-Teaming
Structured red-team exercises targeting your LLM deployments using MITRE ATLAS TTPs and OWASP LLM Top 10 attack scenarios.
MLOps Security Hardening
CI/CD pipeline security for ML workflows, model registry access controls, and infrastructure-as-code security scanning for AI workloads.
All services delivered through a single pane of glass with unified reporting and alerting
What Sets Vimix Apart
Why CISOs and AI leaders choose a purpose-built AI security practice over adapting traditional cybersecurity tools.
| Capability | Vimix Secure AI Adoption | Traditional Security Approach |
|---|---|---|
| Threat coverage | OWASP LLM Top 10, MITRE ATLAS, AI supply chain — purpose-built for AI attack surfaces | Designed for application and network threats — misses AI-specific vectors entirely |
| Regulatory alignment | EU AI Act, NIST AI RMF, ISO/IEC 42001 — current and forward-looking | No native AI regulatory framework coverage |
| Red-team capability | Adversarial LLM testing: prompt injection, jailbreak, RAG poisoning, model inversion | Standard pen testing — not designed for generative AI behaviour |
| Data privacy | Differential privacy, training data anonymization, PII leakage prevention through model outputs | GDPR controls at the data layer — no model-level privacy enforcement |
| Governance | Model cards, bias testing, explainability documentation, board-ready AI risk reporting | No AI governance tooling or documentation frameworks |
| Monitoring | AI-specific: prompt anomaly detection, output drift, API abuse — integrated with your SIEM | Generic log monitoring — cannot detect AI-specific attack patterns |
| Speed to protection | Rapid deployment of guardrails and monitoring within days of engagement start | Months of customization to adapt tools not designed for AI workloads |
Adopt AI at Speed. Without Compromising Security.
Whether you are deploying your first LLM-powered application or scaling a GenAI platform across the enterprise, Vimix ensures every AI initiative is built on a foundation of security, privacy, and governance. Schedule a 45-minute AI Security Assessment with our team.
Frequently Asked Questions
What is the OWASP Top 10 for LLMs and why does it matter?
The OWASP Top 10 for Large Language Model Applications is the industry-standard framework for understanding the most critical security risks in LLM deployments. It covers prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. Every Vimix AI security engagement is assessed against this framework as a baseline.
What is MITRE ATLAS and how does Vimix use it?
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is the AI equivalent of the MITRE ATT&CK framework — a knowledge base of adversary tactics, techniques, and case studies targeting AI and ML systems. We use ATLAS to structure our AI red-team exercises, map identified vulnerabilities to known adversary TTPs, and ensure our security assessments reflect real-world attack patterns rather than theoretical risks.
How does Vimix approach EU AI Act compliance?
The EU AI Act classifies AI systems by risk level and imposes obligations on providers and deployers of high-risk systems. We help organizations conduct AI system classification, perform conformity assessments, implement required technical documentation (including model cards and risk management systems), establish human oversight mechanisms, and build the audit trails required for regulatory scrutiny. We also advise on GPAI model obligations for organizations developing or deploying general-purpose AI.
Can Vimix secure AI systems built on third-party models like GPT-4, Claude, or Gemini?
Yes. The majority of enterprise AI deployments are built on top of third-party foundation models via API. Our security controls operate at the integration layer — input validation, output filtering, API security hardening, prompt management, and monitoring — independent of the underlying model provider. We also assess the security of your RAG pipelines, vector databases, and agent frameworks (LangChain, LlamaIndex, AutoGen) that sit between your application and the foundation model.
What does an AI security red-team engagement involve?
Our AI red-team engagements are structured around your specific deployment: we map your AI system architecture, identify trust boundaries and input/output surfaces, then execute adversarial testing covering direct and indirect prompt injection, jailbreak attempts, context window manipulation, RAG retrieval poisoning, model inversion probing, and multi-turn manipulation chains. All findings are documented with proof-of-concept demonstrations, risk ratings, and validated mitigations.
How does Vimix handle AI security for agentic AI systems?
Agentic AI — systems where LLMs autonomously plan and execute multi-step tasks with access to tools, APIs, and external data — introduces significantly elevated security risks including excessive agency, uncontrolled tool use, and cascading prompt injection through tool outputs. We assess and harden agentic architectures by implementing least-privilege tool access, human-in-the-loop controls for high-risk actions, sandboxed execution environments, and comprehensive audit logging of all agent decisions and actions.
Our Impact
Resource Library
Explore research, insights, guides, and news on secure ai adoption.
Request for
services
Find out more about how we can help your organization navigate its next. Let us know your areas of interest so that we can serve you better.
All the fields marked with * are required.