access-review
FreeConduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
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Automate security workflows and remediation. Build security pipelines, automate compliance checks, and implement SOAR capabilities. Use when scaling security operations or implementing DevSecOps.
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Automate security operations for scale and efficiency.
# .github/workflows/security.yml
name: Security Pipeline
on: [push, pull_request]
jobs:
security:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Secret Scanning
uses: trufflesecurity/trufflehog@main
- name: SAST
uses: returntocorp/semgrep-action@v1
- name: Dependency Scan
run: npm audit --audit-level=high
- name: Container Scan
uses: aquasecurity/trivy-action@master
with:
scan-type: 'fs'
- name: Compliance Check
run: |
checkov -d . --framework terraform
# Auto-remediation script
def remediate_public_s3(bucket_name):
"""Remove public access from S3 bucket."""
s3 = boto3.client('s3')
s3.put_public_access_block(
Bucket=bucket_name,
PublicAccessBlockConfiguration={
'BlockPublicAcls': True,
'IgnorePublicAcls': True,
'BlockPublicPolicy': True,
'RestrictPublicBuckets': True
}
)
playbook:
name: Suspicious Login Response
trigger: alert.type == "suspicious_login"
actions:
- enrich_ip:
source: threat_intel
- if_condition: ip.is_malicious
then:
- block_ip:
firewall: cloudflare
- disable_user:
duration: 1h
- notify:
channel: security
- create_ticket:
priority: high
# Checkov custom check
from checkov.terraform.checks.resource.base_resource_check import BaseResourceCheck
class S3Encryption(BaseResourceCheck):
def __init__(self):
name = "Ensure S3 bucket has encryption enabled"
id = "CUSTOM_S3_1"
supported_resources = ['aws_s3_bucket']
super().__init__(name=name, id=id, ...)
def scan_resource_conf(self, conf):
if 'server_side_encryption_configuration' in conf:
return CheckResult.PASSED
return CheckResult.FAILED
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Conduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
The English summary is being prepared. Showing the original description.
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
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Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.
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Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.
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Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.
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Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
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