Anchore Container Scanner - AI-Optimized Technical Reference

Core Technology Overview

Primary Function: Container vulnerability scanner that generates Software Bill of Materials (SBOM) before scanning for vulnerabilities, unlike traditional scanners that only check for known issues.

Current Version: Enterprise 5.19 (August 2025)

Architecture:

• Built on two open source tools: Syft (SBOM generation) and Grype (vulnerability scanning)
• Enterprise platform adds policy management, web UI, and compliance reporting
• Microservices architecture using PostgreSQL for storage

Configuration - Production-Ready Settings

Resource Requirements (Reality vs Documentation)

Documented minimums (insufficient for production):

• Memory: 1-2GB
• CPU: 1 core
• Storage: 10GB

Actual production requirements:

• Memory: 4-8GB minimum (16GB+ for heavy workloads)
• CPU: 2+ cores
• Storage: 100GB+ (vulnerability database is 600GB+ decompressed)
• PostgreSQL: Requires tuned max_connections and shared_buffers

Critical Configuration Failures

Database Sync Issues:

• Initial vulnerability database sync: 45+ minutes minimum
• Fails with ECONNRESET if connection drops during sync
• Database updates multiple times daily
• Air-gapped environments require manual weekly updates

Memory Allocation Gotchas:

• Default Kubernetes resource limits cause OOMKilled errors
• Large Python images (1GB+) require 8GB+ memory for scanning
• TensorFlow containers can consume 23+ minutes scan time and crash analyzers

Working Kubernetes Configuration

catalog:
  resources:
    requests:
      memory: "4Gi"
      cpu: "2"
    limits:
      memory: "8Gi"
      cpu: "4"

Performance Characteristics

Scan Times by Image Type

• Typical Node.js container: 30 seconds
• Large Python containers: 5-15 minutes
• ML/TensorFlow images: 15-25 minutes (high failure rate)

CI/CD Integration Delays

• GitHub Action downloads 150MB vulnerability database per run
• Default timeout insufficient for large images (need 15+ minute timeouts)
• Parallel scanning reduces build blocking but increases resource usage

Critical Warnings - Production Failure Modes

Policy Enforcement Breaking Deployments

Severity Level Impact:

• severity-cutoff: medium fails 90% of deployments due to ancient OpenSSL CVEs
• First policy deployment breaks existing applications
• False positive rate: ~80% for medium severity findings

Rollout Strategy That Works:

1. Week 1-2: Monitor mode only
2. Week 3-4: Non-production enforcement
3. Week 5+: Production with escape hatches
4. Start with severity-cutoff: high and fail-build: false

Air-Gapped Deployment Challenges

Time Investment: 2-3x longer setup than connected deployments
Common Failures:

• Certificate management in proxy environments
• Container image mirroring complexity
• Manual database update procedures

Decision Support - Cost vs Value Analysis

Pricing Reality

• Enterprise: $10-20k annually (small teams) to six figures (large orgs)
• Pricing model: Per SBOM processed (scales expensive quickly)
• No public pricing (requires sales contact)

Free vs Paid Decision Matrix

Requirement	Open Source Tools (Free)	Enterprise ($$$)
Basic vulnerability scanning	✅ Syft + Grype sufficient	❌ Overkill
SBOM generation	✅ Syft handles this well	❌ Unnecessary
Policy enforcement	❌ Manual implementation	✅ Required feature
FedRAMP/DoD compliance	❌ Insufficient	✅ Primary use case
Multi-team management	❌ No access controls	✅ Essential
Compliance reporting	❌ Manual process	✅ Automated theater

Competitive Analysis - When to Choose What

Choose Anchore Enterprise if:

• Government contracts requiring FedRAMP/STIG compliance
• Need automated compliance reporting for auditors
• Budget available and tired of managing scanner infrastructure
• SBOM tracking is regulatory requirement

Choose Alternatives if:

• Trivy: Basic scanning, most teams, no compliance requirements
• Snyk: Developer-focused workflows, small teams
• Aqua Security: Runtime protection more important than SBOM
• Prisma Cloud: Unlimited budget, comprehensive security needs

Implementation Reality - Common Pitfalls

Language/Ecosystem Support Quality

Reliable Support:

• Java (Maven/Gradle)
• JavaScript (npm/yarn)
• Python (pip)
• Go modules

Problematic Areas:

• Custom build systems
• Monorepo setups with unusual package managers
• Custom Maven configurations (frequent failures)

False Positive Management Workload

Daily Tasks:

• Reviewing and suppressing false positives
• Tuning content hints for specific packages
• Managing vulnerability corrections per environment
• Policy adjustment for new applications

Time Investment:

• First month: Full-time equivalent managing suppressions
• Ongoing: 3-5 hours weekly for policy maintenance

Kubernetes Integration Gotchas

Admission Controller Issues:

• Start in "warn" mode, not "block" mode
• Will break production deployments if misconfigured
• Network connectivity issues between microservices common

Ingress Configuration:

• Default ingress config incompatible with most controllers
• Results in 503 Service Temporarily Unavailable errors
• Requires custom annotations per environment

Resource Requirements - Time and Expertise

Setup Time Investment

• Connected deployment: 1-2 days
• Air-gapped deployment: 1-2 weeks
• Policy tuning: 4-6 weeks to production-ready
• Team training: 2-3 days for operational staff

Expertise Requirements

• Kubernetes administration (intermediate level)
• PostgreSQL tuning knowledge
• JSON policy syntax understanding
• Container registry authentication management
• Vulnerability management processes

Operational Overhead

• Database maintenance: Weekly
• Policy updates: Daily during initial months
• False positive management: Ongoing daily task
• Version upgrades: Quarterly, require testing

Breaking Points and Failure Thresholds

Scale Limitations

• UI becomes unusable above 1000+ container images
• Database performance degrades with >10k scans
• Large container scans (4GB+) frequently timeout or crash

Known Version 5.19 Issues

• One-time scan feature occasionally returns empty results for large scans
• STIG compliance checking fails silently without Cinc installation
• Web UI links frequently broken (use API instead)

Infrastructure Dependencies

• PostgreSQL required (no alternative storage backends)
• Persistent storage mandatory or scan history lost
• Network connectivity essential for vulnerability database updates

Migration and Integration Costs

From Existing Scanners

• Policy recreation required (no import functionality)
• Historical scan data cannot be migrated
• Team retraining on new policy syntax
• Integration testing with existing CI/CD pipelines

Hidden Implementation Costs

• Extended troubleshooting time for air-gapped environments
• Policy tuning consultant time (often required)
• Additional infrastructure for proper resource allocation
• Ongoing false positive management overhead

Support and Community Quality

Support Characteristics

• Enterprise support quality above industry average
• Open source community active and helpful
• Documentation quality exceptional for security tools
• Response times reasonable for paid support

Community Resources Value

• GitHub issues contain real solutions to common problems
• Open source tools well-maintained and actively developed
• Enterprise roadmap responsive to compliance requirements

Critical Success Factors

Deployment Prerequisites

• Kubernetes cluster with 16GB+ available memory per node
• PostgreSQL expertise or managed service
• Network architecture supporting microservices communication
• Compliance team buy-in for policy development

Operational Requirements

• Dedicated team member for policy management (at least 25% FTE)
• Established vulnerability management processes
• Container registry with proper authentication configured
• Monitoring and alerting for scanner infrastructure health