Accelerate Market Growth
two lines
Monolithic architectures slow time-to-market, limiting your ability to activate new use cases and seize emerging opportunities. Modern, flexible platforms enable rapid iteration and improved scalability.
Accelerate Market Growth
two lines
Monolithic architectures slow time-to-market, limiting your ability to activate new use cases and seize emerging opportunities. Modern, flexible platforms enable rapid iteration and improved scalability.
Identity & Access for AI Agents
Identity is the control plane for AI. Every model, agent, and workflow operates through enterprise permissions—and at scale, this becomes the most critical control layer.
Foundational requirements
Centralize authentication under a single identity provider
Enforce phishing-resistant MFA or passkeys
Implement risk-based conditional access
Eliminate shared and unmanaged accounts
Enforce least privilege
Additional AI requirements
Assign each model and agent a unique workload identity
Use short-lived credentials with automated rotation
Deploy workload identity frameworks (e.g., SPIFFE/SPIRE)
Monitor agent activity as privileged behavior
Require approvals for high-impact actions
AI Data Security & Governance
AI systems derive value from enterprise data, making data protection central to AI security.
Foundational requirements
Deploy DLP across endpoints and cloud
Encrypt data in transit and at rest
Classify sensitive data
Define governance and retention policies
Monitor data movement
Additional AI requirements
Inspect prompts and outputs for sensitive data exposure
Restrict regulated data in unapproved models
Enforce least-privilege dataset access
Validate training datasets
Maintain data lineage
As AI adoption grows, organizations must assume that sensitive data will flow through AI systems—and design controls accordingly.
Securing AI Cloud Infrastructure
Most AI systems operate in cloud environments where training and inference run at scale.
Foundational requirements
Establish secure configuration baselines
Enforce policy-as-code guardrails
Use private endpoints and network isolation
Deploy CSPM
Centralize logging and monitoring
Additional AI requirements
Isolate development, training, and production environments
Constrain agent execution with scoped IAM roles
Validate model artifacts before deployment
Log agent-initiated actions
Implement runtime guardrails
Autonomous systems require not just secure infrastructure, but clearly enforced execution boundaries.
AI Software Supply Chain Security
AI systems rely on open-source frameworks, container images, and rapidly evolving tooling.
Foundational requirements
Maintain asset inventories
Conduct continuous vulnerability scanning
Apply risk-based patching
Integrate security into DevSecOps
Enforce patch governance
Additional AI requirements
Monitor ML framework advisories
Rebuild and rescan AI images regularly
Validate third-party AI providers
Review dependencies before deployment
Keep AI systems within patch cycles
AI pipelines must be treated as critical software supply chains—not experimental tooling.