AI Security Services Overview
AI security is the practice of protecting AI systems themselves: the models, the prompts, the training data and the pipelines that deploy them. It is a different discipline from securing your network, and most security programs do not cover it yet.
Exquitech secures AI systems for organizations in the UAE, Saudi Arabia, and UK. We classify your AI by risk, test models for adversarial weakness, control what prompts can do, secure the MLOps pipeline, and give your security team a way to detect and respond to AI incidents.
Where this sits next to your other work:
- Your estate: Cybersecurity risk management for Sentinel, Defender and incident response
- Your standards: Cybersecurity consulting for ISO 27001 and NCA ECC
- Your AI program: AI readiness assessment for strategy, governance and adoption
This page is for securing the AI systems themselves.
What our AI Security Services Cover
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AI security strategy and governance
- Establishes a comprehensive AI security strategy, including vision, mission, and roadmap, shaped by our AI readiness consultants.
- Emphasizes AI security governance with quality management, procedures, and classification systems.
- Focuses on AI security compliance, addressing unacceptable and high-risk systems.
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AI model security
- Covers model discovery, classification, and loss prevention.
- Addresses adversarial attacks and implements fairness controls to ensure robust and unbiased models.
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AI core security
- Enforces data security rules and compliance with regulatory requirements.
- Ensures secure data preparation and application security, including identity and access management.
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AI technical foundation
- Focuses on intent recognition, prompt engineering, and input/output security.
- Highlights the importance of secure coding practices and validation for AI applications.
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AI operations security
- Details incident detection and response, cloud and infrastructure security, and regular security measures.
- Includes MLOps security and supply chain risk management to maintain operational integrity.
Customer challenges
Exquitech AI customers are faced with a myriad of threats, challenges and opportunities they seek to address.
Data privacy and protection
Safeguarding sensitive data and ensuring compliance through secure data pipelines and embedded privacy controls.
Ethical and transparent AI
Addressing bias, fairness, and explainability through responsible AI frameworks and governance structures.
Model robustness and trust
Mitigating adversarial threats and enhancing model reliability via classification, validation, and resilience testing.
Secure integration and legacy compatibility
Enabling smooth, secure AI deployment within existing environments using identity, access, and validation protocols.
Operational scalability
Scaling AI with confidence through secure MLOps, supply chain protection, and performance safeguards.
Incident detection and response
Proactively managing AI-related threats with real-time detection, response playbooks, and monitoring systems.
Our comprehensive adoption journey addresses all these challenges, and more.
How our AI security engagements run
AI Security Strategy & Governance
A strategy is set to align AI use with business goals. Governance frameworks and performance tracking ensure accountability.
AI Technical Foundation
Secure coding and validation practices are applied to AI systems. Governance frameworks and safeguards ensure prompts and outputs function safely.
AI Core Security
Data protection and access controls safeguard sensitive information. Secure workflows protect training and deployment pipelines.
AI Model Security
Models are classified and secured against loss or attack. Fairness and reliability are maintained through strong defenses.
AI Operations Security
AI environments are monitored for threats and incidents. MLOps and supply chains are reinforced to maintain resilience.
AI Security Strategy & Governance
A strategy is set to align AI use with business goals. Governance frameworks and performance tracking ensure accountability.
AI Model Security
Models are classified and secured against loss or attack. Fairness and reliability are maintained through strong defenses.
AI Core Security
Data protection and access controls safeguard sensitive information. Secure workflows protect training and deployment pipelines.
AI Technical Foundation
Secure coding and validation practices are applied to AI systems. Governance frameworks and safeguards ensure prompts and outputs function safely.
AI Operations Security
AI environments are monitored for threats and incidents. MLOps and supply chains are reinforced to maintain resilience.
Customer benefits
Exquitech Secure AI adoption & readiness journey clients benefit from an array of business outcomes
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Enhanced data security
Safeguarding sensitive data and ensuring compliance through secure data pipelines and embedded privacy controls.
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Ethical AI practices
Addressing bias, fairness, and explainability through responsible AI frameworks and governance structures.
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Improved model reliability
Mitigating adversarial threats and enhancing model reliability via classification, validation, and resilience testing.
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Risk based controls
Navigating evolving legal requirements with structured AI compliance frameworks and risk-based controls.
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Seamless integration
Enables smooth integration of AI with existing legacy systems, minimizing disruptions and enhancing operational continuity.
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Provable AI controls
Evidence of model testing, prompt controls and access decisions, in the format auditors and regulators ask for.
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Cost management
Optimizes resource allocation and reduces development and operational costs through efficient AI security strategies and governance.
Is your AI secure enough to go live?
Tell us what models and agents you are running and we will show you where the exposure is.
Use Cases
Enterprise AI security vision and roadmap definition
Classification of AI systems by risk level
AI governance framework aligned with compliance standards
Model discovery and classification tooling
Fairness validation and adversarial robustness testing
AI model loss prevention integration
Data classification and labelling for AI pipelines
Identity and access control for AI workloads
Secure data preparation for training and inference
Prompt engineering risk analysis and control
Input/output validation for generative AI applications
Secure coding and CI/CD pipeline for AI systems
MLOps security and model integrity validation
AI incident detection and response orchestration
AI supply chain and infrastructure risk assessment
Blogs
Related Capabilities
Cybersecurity Risk Management
Sentinel, Defender XDR and incident response for the estate your AI systems run on.
AI Readiness Consulting
Strategy, governance and adoption planning before you scale AI across the business.
Microsoft Foundry and AI Agents
Build and govern custom agents on Microsoft Foundry, Copilot Studio and Agent 365.
Get a consultation
from an expert
Our experts are ready, experienced and tooled to help your business address its secure AI adoption challenges. Let’s chat.