Cloud Security Solutions for the Data-to-AI Pipeline

Defaults are not a security strategy.

AI cloud security starts with knowing what you have and how it is configured. Enterprise AI platforms ship with default settings and AI adoption often spreads across business units faster than security can review those settings for compliance. Our approach to AI security posture management (AI-SPM) gives you a clear baseline and a path to close the gaps.

• Discover AI assets, including shadow AI outside sanctioned tools
• Evaluate content filtering, model access controls and responsible AI alignment
• Prioritize fixes with an AI risk heat map and a governance roadmap

Your traditional security tools protect the infrastructure; who protects the model?

AI workloads introduce risks traditional cloud security tools weren’t built to detect. Traditional security solutions, including many CNAPPs, secure the infrastructure AI runs on, but not model behavior, guardrails or the data a model can reach. Prompt injection, training data poisoning and unmonitored endpoints create exposure that grows with every deployment.

• Assess exposure to prompt injection, model poisoning and unprotected endpoints
• Implement production guardrails, content filtering and policy as code
• Validate controls with AI red teaming and adversarial testing

Every stage of the model lifecycle is an attack surface.

ML infrastructure spans training, versioning, deployment and inference, all of which fall outside the scope of traditional security reviews. As AI agents begin acting on model outputs, the identities and permissions behind them become part of that surface too. We map where controls are missing across your data-to-AI pipeline and build the right ones in.

• Review MLOps maturity, model registry access and pipeline integrity
• Design MLOps architecture and model risk controls
• Govern the non-human identities and AI agents acting on model outputs

Findings without fixes don’t reduce risk.

Many organizations own security tools that were never fully deployed and pipelines that were built for speed rather than security. Our cloud security engineers work alongside your team to close those gaps using cloud-native security principles, so every control is versioned, auditable and repeatable.

• Deploy the security tools you already own, tuned into one strategy
• Rebuild data and AI pipelines with encryption and access policies built in
• Hand off runbooks, dashboards and training so your team owns the result

One standard across every cloud.

Cloud environments change constantly: new services, new regions and emerging AI regulations. When each platform team sets its own rules, gaps open at the boundaries. Our cloud security governance and architecture expertise gives you one consistent approach, connected to your broader risk, compliance and identity programs.

• Establish frameworks, configuration standards and roadmaps for every cloud and SaaS environment
• Align to emerging AI regulations and the frameworks you already report against
• Connect data and AI security to your enterprise GRC and IAM programs