Am I the only one who finds the security findings too noisy to be useful?

Having recently conducted a comprehensive evaluation of Amazon Q Developer for my organization's CI/CD pipeline integration, I must express a significant point of contention: the signal-to-noise ratio in its security vulnerability findings is, in my professional assessment, critically imbalanced. The tool appears to prioritize generating a high volume of findings over delivering contextually actionable intelligence, leading to alert fatigue and potentially causing genuine critical issues to be buried.

My primary observation stems from its analysis of Terraform configurations for AWS. Q Developer frequently flags what I would classify as "theoretical" or "context-agnostic" risks. For instance, it will uniformly flag any S3 bucket without explicit `block_public_access` settings, without considering the surrounding network architecture. A bucket behind a VPC endpoint, accessible only via a private subnet with stringent security group rules, presents a materially different risk profile than an internet-facing bucket, yet both receive identical severity alerts.

Consider this simplified module:
```hcl
module "internal_data_lake" {
source = "./modules/s3-private"

bucket_name = "company-internal-data"
vpc_id = var.vpc_id

# Q consistently flags the lack of block_public_access here
# despite the following context...
}

resource "aws_vpc_endpoint" "s3" {
vpc_id = var.vpc_id
service_name = "com.amazonaws.${var.region}.s3"
vpc_endpoint_type = "Gateway"
route_table_ids = [aws_route_table.private.id]
}
```
The finding is technically correct in isolation but operationally misleading given the enforced network path.

Furthermore, the findings for container images exhibit similar behavior. It will list every CVE from base OS packages in a distroless image, many of which are not exploitable because the affected binary or library is not present in the final runtime container. This creates a colossal list that DevOps engineers must manually triage, defeating the purpose of automated security scanning.

* **Lack of Runtime Context:** Findings are derived from static code analysis without incorporating the intended runtime environment (VPCs, security groups, IAM boundary conditions).
* **Inconsistent Severity Calibration:** A missing S3 encryption flag and a hardcoded IAM secret in a Lambda environment variable are often presented with similar urgency.
* **Remediation Overload:** The suggested fixes are frequently generic "enable setting X" without providing the necessary Terraform or CloudFormation snippet that integrates with the existing module structure, leading to more research for the engineer.

I am curious if other practitioners have developed effective filtering strategies or contextual rule configurations to make these outputs more manageable. Have you integrated Q's findings with a policy-as-code engine like OPA/Conftest or a dedicated security orchestration platform to apply business logic to these alerts? Or are we collectively accepting the noise as the cost of doing business with such a tool?

--from the trenches