AI & AUTOMATION
Azure & Microsoft AI Security Solutions
Advanced workload defense, adversarial red teaming, and data loss prevention built for Microsoft AI environments.
Azure & Microsoft AI Security Solutions
Secure the systems, models, and data flows that power enterprise AI. We protect AI workloads, applications, and custom agents across Microsoft Azure and Microsoft 365 with zero-trust architecture.
What Is Enterprise AI Security?
Enterprise AI security is the technical practice of designing, defending, testing, and monitoring artificial intelligence workloads, foundational models, data retrieval pipelines, and autonomous agents against malicious exploitation and unauthorized data leakage. It addresses AI-specific vulnerabilities—such as direct and indirect prompt injection, jailbreaking, model poisoning, unauthorized retrieval exfiltration, and excessive agent tool authority—by implementing defense-in-depth controls across cloud networks, identity systems (Entra ID), data governance (Purview), and continuous red-team testing.
Microsoft + AI Security Solutions
Deploy AI confidently with security, governance, compliance, and operational controls designed for enterprise-scale environments.
- AI Risk & Security Assessments: Evaluate risks across models, agents, data sources, integrations, and users.
- Data Protection & Privacy Validation: Verify controls that safeguard sensitive data and prevent unauthorized disclosure.
- Agent & Workflow Security Reviews: Ensure AI agents and automations cannot perform unauthorized actions or circumvent controls.
- Governance & Compliance Readiness: Validate alignment with corporate policies, regulatory requirements, and industry standards.
- Continuous Monitoring & Assurance: Establish ongoing visibility into AI behavior, risk posture, and operational compliance.
- Executive AI Strategy & Risk Advisory: Provide guidance for secure, responsible, and value-driven AI adoption at scale.
The 7 Layers of AI Workload Security
A simple, vendor-neutral framework to secure AI from development to daily use.
| Security Layer | Vulnerability Prevented | Nexigen Technical Control |
|---|---|---|
| 1. Governance & Risk | Uncontrolled or non-compliant AI adoption. | Set policies, standards, and oversight for responsible AI use. |
| 2. Identity & Access | Unauthorized access and privilege misuse. | Manage who can use AI tools, access data, and perform actions. |
| 3. Data Security | Data leakage and exposure of confidential information. | Protect sensitive data in prompts, outputs, and training data. |
| 4. Application & Model Security | Prompt injection, model manipulation, and insecure configurations. | Secure AI applications, models, and integrations across the lifecycle. |
| 5. Shadow AI & Usage Control | Unmanaged AI tools and risky or inappropriate use. | Discover and manage unsanctioned AI tools and monitor how AI is being used across the organization. |
| 6. Infrastructure & Network Security | Exposure to the internet and lateral movement in AI environments. | Protect the underlying infrastructure, networks, and environments that run AI workloads. |
| 7. Monitoring & Detection | Undetected abuse, policy violations, and emerging threats. | Gain visibility into AI activity, detect threats and misuse, and respond quickly. |
Azure & Microsoft AI Security Solutions
Secure the systems, models, and data flows that power enterprise AI. We protect AI workloads, applications, and custom agents across Microsoft Azure and Microsoft 365 with zero-trust architecture.
What Is Enterprise AI Security?
Enterprise AI security is the technical practice of designing, defending, testing, and monitoring artificial intelligence workloads, foundational models, data retrieval pipelines, and autonomous agents against malicious exploitation and unauthorized data leakage. It addresses AI-specific vulnerabilities—such as direct and indirect prompt injection, jailbreaking, model poisoning, unauthorized retrieval exfiltration, and excessive agent tool authority—by implementing defense-in-depth controls across cloud networks, identity systems (Entra ID), data governance (Purview), and continuous red-team testing.
Microsoft + AI Security Solutions
Deploy AI confidently with security, governance, compliance, and operational controls designed for enterprise-scale environments.
- AI Risk & Security Assessments: Evaluate risks across models, agents, data sources, integrations, and users.
- Data Protection & Privacy Validation: Verify controls that safeguard sensitive data and prevent unauthorized disclosure.
- Agent & Workflow Security Reviews: Ensure AI agents and automations cannot perform unauthorized actions or circumvent controls.
- Governance & Compliance Readiness: Validate alignment with corporate policies, regulatory requirements, and industry standards.
- Continuous Monitoring & Assurance: Establish ongoing visibility into AI behavior, risk posture, and operational compliance.
- Executive AI Strategy & Risk Advisory: Provide guidance for secure, responsible, and value-driven AI adoption at scale.
The 7 Layers of AI Workload Security
A simple, vendor-neutral framework to secure AI from development to daily use.
| Security Layer | Vulnerability Prevented | Nexigen Technical Control |
|---|---|---|
| 1. Governance & Risk | Uncontrolled or non-compliant AI adoption. | Set policies, standards, and oversight for responsible AI use. |
| 2. Identity & Access | Unauthorized access and privilege misuse. | Manage who can use AI tools, access data, and perform actions. |
| 3. Data Security | Data leakage and exposure of confidential information. | Protect sensitive data in prompts, outputs, and training data. |
| 4. Application & Model Security | Prompt injection, model manipulation, and insecure configurations. | Secure AI applications, models, and integrations across the lifecycle. |
| 5. Shadow AI & Usage Control | Unmanaged AI tools and risky or inappropriate use. | Discover and manage unsanctioned AI tools and monitor how AI is being used across the organization. |
| 6. Infrastructure & Network Security | Exposure to the internet and lateral movement in AI environments. | Protect the underlying infrastructure, networks, and environments that run AI workloads. |
| 7. Monitoring & Detection | Undetected abuse, policy violations, and emerging threats. | Gain visibility into AI activity, detect threats and misuse, and respond quickly. |
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