Ermetic vs Solvo: Automated Least Privilege for AI

Ermetic excels at identity-centric risk discovery because its graph-based engine correlates human and non-human identities with their effective permissions, network exposure, and data sensitivity. For example, in a typical AWS environment with SageMaker training jobs accessing S3, Ermetic can map the exact blast radius of a compromised service role by analyzing the toxic combination of s3:GetObject and s3:PutObject permissions alongside public network paths. This results in a prioritized remediation plan that reduces the identity attack surface by an average of 90% within the first week of deployment, according to their published benchmarks.

Solvo takes a fundamentally different approach by prioritizing data-centric security automation. Instead of starting with the identity, Solvo's engine analyzes the data an AI workload actually accesses at runtime and generates a least-privilege policy based on observed behavior. For instance, if a containerized LLM agent only reads from a specific training-data prefix in an S3 bucket, Solvo automatically creates an IAM policy scoped to that exact prefix and action, ignoring all other unused entitlements. This results in a policy that is perfectly tailored to operational reality, eliminating the guesswork of static analysis.

The key trade-off: If your priority is comprehensive risk visibility and understanding complex toxic access combinations across a multi-cloud estate, choose Ermetic. Its graph-based analysis is superior for security teams needing to audit and report on the overall identity posture before an incident occurs. If you prioritize hands-off, runtime-accurate policy generation that adapts dynamically to AI workload behavior without manual review, choose Solvo. Its data-centric automation is ideal for DevOps and platform teams embedding security directly into the CI/CD pipeline for containerized AI microservices.

Ermetic excels at visualizing and deconstructing complex identity relationships because its graph-based engine maps every human and non-human identity to its effective permissions. For example, in an AI pipeline where a SageMaker training job accesses an S3 bucket, Ermetic automatically surfaces the toxic combination of a publicly exposed role with s3:GetObject on a sensitive data lake, reducing the attack surface by up to 90% through its automated right-sizing policies.

Solvo takes a fundamentally different approach by prioritizing the data object itself. Instead of starting with the identity, Solvo's runtime analysis profiles the exact data access patterns of an AI agent, such as a Lambda function querying a DynamoDB table. This results in a security policy that is hyper-specific to the data's sensitivity level, creating a 'data-centric security perimeter' that automatically restricts access based on the content's classification, not just the IAM role's broad permissions.

The key trade-off: If your priority is gaining immediate visibility into identity sprawl and eliminating unused entitlements across thousands of machine roles, choose Ermetic. If you prioritize enforcing granular, content-aware access controls where the security posture is defined by the data's sensitivity rather than the identity's configuration, choose Solvo.