InfraShield leverages high-throughput LLM reasoning models to continuously analyze infrastructure-as-code, audit live cloud state, enforce zero-trust IAM policies, and automatically remediate vulnerabilities before deployment.
Traditional static analysis engines rely on rigid rules that miss context and produce high false-positive rates. InfraShield introduces real-time semantic inference to evaluate infrastructure context holistically.
Evaluates Terraform, OpenTofu, and CloudFormation against deep-reasoning LLMs. Understands cross-resource dependencies and data flows rather than checking isolated syntax.
Translates complex cloud regulatory frameworks (SOC2, ISO27001, HIPAA) into automated fine-tuned prompts and runtime guards using AWS Bedrock foundation models.
Generates accurate, clean Pull Requests to fix detected vulnerabilities in your GitHub or GitLab repository, reducing engineering friction and mean time to remediate.
InfraShield runs high-frequency model inference on Amazon Bedrock (Claude 3.5 Sonnet / Llama 3) and fine-tuned domain models on Amazon SageMaker to ensure enterprise-grade throughput and data privacy.
Analyzes CloudTrail log telemetry against infrastructure code to identify over-privileged wildcard permissions (`*`) and automatically crafts zero-trust JSON policy limits.
Integrates into CI/CD pipelines via GitHub Actions, GitLab CI, and CLI tools, auditing infrastructure code in milliseconds before deployment.
Uses vector similarity search and pattern matching to identify hardcoded credentials, unencrypted buckets, and unmapped cloud resources across multi-account deployments.
Generates instant compliance audit reports for CIS AWS Benchmarks, PCI-DSS, SOC2, and ISO27001 with actionable step-by-step guidance.
We are onboarding select engineering teams to test our high-throughput AI infrastructure auditing engine. Contact our founding team to request a custom deployment demo.