AI security architecture
Design security-conscious architectures for enterprise AI applications, considering data flows, trust boundaries, integrations, identities and deployment environments.
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Secure AI
Build AI systems with security at their core.
AI introduces new security considerations across models, enterprise data, agents and connected systems. We help organizations identify risks, design appropriate controls and incorporate security into the AI engineering lifecycle.
The security challenge
AI creates new trust boundaries to protect.
Traditional application security remains important, but AI systems introduce additional risks through natural-language interfaces, model behavior, retrieval pipelines, autonomous workflows and external integrations.
01
Sensitive enterprise data being exposed through AI interactions.
02
Prompt injection and untrusted content influencing application behavior.
03
AI agents operating with excessive permissions or insufficient oversight.
04
Limited visibility into model interactions, tool execution and security events.
Our capabilities
Security across the AI application lifecycle.
We consider security from architecture and development through deployment and ongoing operation, with controls aligned to the system's use case and risk profile.
AI threat modeling and risk assessment
Identify AI-specific threats, attack surfaces and potential failure scenarios across models, prompts, retrieval systems, agents and connected tools.
RAG and data access security
Address data exposure risks through access-aware retrieval, permission enforcement, tenant isolation and appropriate controls around enterprise knowledge sources.
Agent and tool security
Define boundaries for AI agents and their tools, including least-privilege access, controlled execution, human approval for sensitive actions and activity monitoring.
AI governance and controls
Establish practical governance considerations covering acceptable use, accountability, oversight, risk ownership and security requirements across AI systems.
Monitoring and security validation
Consider logging, monitoring, evaluation and security testing to help identify unexpected behavior, policy violations and emerging risks.
Areas of consideration
Understand the risks before they become incidents.
Security reviews can consider a range of AI-specific risks depending on the architecture, application and deployment context.
Prompt injection
Malicious or untrusted instructions influencing model behavior or downstream actions.
Sensitive data exposure
Inappropriate access to confidential information through prompts, responses, retrieval or integrations.
Excessive agency
AI agents receiving broader permissions or taking actions beyond their intended scope.
Insecure integrations
Weak boundaries around APIs, tools, identity systems and connected enterprise services.
Insufficient monitoring
Limited visibility into AI activity, security events, policy violations and unexpected behavior.
Supply chain and model risks
Risks introduced by models, datasets, libraries, plugins and other components in the AI ecosystem.
Our security considerations can be informed by relevant industry guidance, including the OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework and MITRE ATLAS. These references are guidance sources, not claims of certification or compliance.
Our approach
Practical controls. Proportionate to risk.
Security should support the intended use of an AI system without becoming an afterthought or an obstacle to useful engineering.
Understand
Map data flows, users, trust boundaries and intended AI capabilities.
Assess
Identify threats, vulnerabilities and potential business impact.
Protect
Design appropriate access controls, guardrails and oversight.
Monitor
Plan validation, logging and ongoing security improvement.
Security by design
Is your AI architecture ready for enterprise security requirements?
Let's discuss your AI use cases, security concerns and the controls needed to support responsible deployment.