Orca Security

This skill covers Orca Security — the cloud security platform that pioneered SideScanning technology for agentless workload scanning. Orca provides a comprehensive CNAPP covering CSPM, CWPP, CIEM, DSPM, API security, and AI-SPM without requiring agents in customer workloads.

How to Approach Tasks

When you receive a request:

Classify the request type:
- Deployment/Onboarding -- Cover SideScanning setup, cloud account connection, scanning scope
- Risk Prioritization -- Explain Orca Risk Score, context-aware prioritization, attack paths
- CSPM/Compliance -- Cover misconfiguration detection, compliance frameworks, reporting
- CWPP/Vulnerability -- Cover vulnerability management, workload scanning, image scanning
- CIEM/Identity -- Cover IAM analysis, net-effective permissions, identity risk
- DSPM/Data -- Cover data discovery, classification, sensitive data exposure
- CI/CD Integration -- Cover shift-left scanning, registry scanning, pipeline integration
- Alert Management -- Cover alert triage, suppression, integrations
Identify environment -- Which cloud providers (AWS, Azure, GCP)? Multi-account? What compliance requirements? Existing security tooling?
Analyze -- Apply Orca-specific reasoning. Orca's key differentiator is agentless SideScanning with a unified data model that correlates across CSPM, CWPP, CIEM, and DSPM without agent complexity.
Recommend -- Provide specific, actionable guidance with Orca platform context.

SideScanning Technology

How SideScanning Works

Orca's SideScanning is the core differentiator — it scans workload storage without running any code in the workload:

Mechanism:

Orca reads cloud storage snapshots (EBS snapshots, Azure managed disk snapshots, GCS disk snapshots) — the same data that backs running VMs and containers
Snapshots are mounted in Orca's own cloud infrastructure as read-only volumes
Orca's scanning engine reads the filesystems, package databases, configuration files, and sensitive data
Snapshots are immediately deleted after scanning

What SideScanning covers:

Installed OS packages (DEB, RPM, APK, Alpine, Windows) → CVE detection
Language runtime packages (npm, pip, gem, Maven, NuGet, Go, etc.) → CVE detection
Configuration files → misconfiguration detection, secrets detection
Sensitive data on disk → PII, PCI, PHI, credentials classification
File integrity and malware → static malware signatures in files
Active processes at time of snapshot (from /proc and OS state files)
User accounts, SSH keys, cron jobs → persistence indicators

What SideScanning does NOT cover:

Real-time runtime behavioral detection (what a process does at execution time)
In-memory threats
Network-level behavioral anomalies in real time
Ephemeral container activity not captured in the snapshot

Performance impact: Zero. Scanning happens outside the workload on a read-only copy. No CPU, memory, or network load on running workloads.

Scanning frequency: Configurable; default is every 24 hours. Near-real-time for newly deployed workloads.

Comparison to Agent-Based CWPP

Capability	Orca SideScanning	Agent-Based (Defender, Aqua)
Vulnerability scanning	Yes (offline snapshot)	Yes (agent or snapshot)
Configuration analysis	Yes	Yes
Secrets on disk	Yes	Yes
Sensitive data classification	Yes	Varies
Real-time runtime detection	No	Yes
Process behavioral analytics	No (snapshot-time only)	Yes
Zero performance impact	Yes	Near-zero (agent has small overhead)
Deployment complexity	Low (no agents)	Medium (agent lifecycle management)

Orca Unified Data Model

Orca builds a unified data model across all discovered assets:

Asset types: Cloud accounts, VMs, containers, managed services (RDS, S3, etc.), IAM entities, network resources, data stores

Relationship types:

Network connectivity (can reach, internet-exposed)
IAM relationships (has role, can assume, has permissions)
Data relationships (stores, classifies as)
Vulnerability relationships (has CVE, severity, exploitability)
Infrastructure relationships (runs on, deployed in)

This unified model enables cross-domain risk correlation — the same approach as Wiz's Security Graph but using Orca's terminology.

Risk Prioritization

Orca Risk Score

Orca uses context-aware risk scoring to prioritize findings:

Risk score factors:

Exploitability: Is there a public exploit? CVSS score? CISA KEV?
Internet exposure: Can this asset be reached from the internet?
Blast radius: What sensitive assets are reachable after compromise?
Asset value: Is this a production system? Does it hold sensitive data?
Attack path depth: How many steps to reach a critical asset?

The result: An Orca risk score (0-100) that represents actual business risk — not just raw vulnerability count or CVSS scores in isolation.

Why this matters:

A CVSS 9.8 CVE on an isolated dev VM with no network exposure = low risk
A CVSS 6.5 CVE on an internet-exposed production VM with an IAM role that can access the customer database = high risk

Attack Paths and Toxic Combinations

Orca visualizes multi-hop attack paths:

Example attack path:

[Internet]
  → EC2 instance (port 8443 open)
    → Exploitable vulnerability (CVE-2023-XXXX, CVSS 9.1)
      → IMDSv1 enabled (credential theft)
        → EC2 instance role
          → S3:* permission on prod bucket
            → PII data: 500K records

Orca labels this chain as a "toxic combination" and scores it based on the full path, not individual steps.

CSPM Capabilities

Configuration checks:

1,500+ built-in checks across AWS, Azure, GCP
Coverage: IAM, networking, storage, compute, databases, logging/monitoring, encryption, container services
Custom checks using Orca's query language

Compliance frameworks:

CIS Benchmarks (AWS, Azure, GCP — all levels)
NIST 800-53, PCI DSS, HIPAA, SOC 2, ISO 27001
GDPR, FedRAMP, CMMC, Australian ISM
Custom frameworks via control mapping

Compliance reporting:

Per-framework compliance score with trend tracking
Resource-level compliance evidence
Exportable audit reports (PDF, CSV)
Scheduled report delivery

CWPP Capabilities

Vulnerability management:

OS and language package CVE detection via SideScanning
EPSS scoring (Exploit Prediction Scoring System) alongside CVSS
CISA KEV integration (flag known-exploited vulnerabilities)
Virtual patching guidance (workarounds while waiting for patches)
Vulnerability age tracking (how long has this been open?)

Container image scanning:

Registry scanning (ECR, ACR, GCR, Docker Hub, private registries)
CI/CD integration (scan images before push/deploy)
Running container scanning via host snapshot
Image layer analysis (identify which layer introduced a CVE)

Malware detection:

Static malware signature scanning on disk
Detects: crypto miners, ransomware, web shells, backdoors, rootkits
Updated threat intelligence feeds

CIEM Capabilities

Identity analysis:

Enumerates all cloud identities across connected accounts
Calculates net-effective permissions (evaluating all policy sources)
Identifies over-privileged identities vs. actually-used permissions
Detects: stale credentials, cross-account trust risks, shadow admins

Identity attack paths:

Maps identity-based attack chains
"This service account can assume this role which can access this sensitive database"
Visualizes identity relationships in the attack path graph

DSPM Capabilities

Data discovery:

Discovers all data stores: S3, Azure Blob, GCS, RDS, DynamoDB, Cosmos DB, Azure SQL, BigQuery, Snowflake, file shares
Identifies data stores not previously known to security teams (shadow data)

Data classification:

PII: names, SSNs, emails, phone numbers, addresses, dates of birth, passport numbers
PCI: credit card numbers, CVVs
PHI: health records, diagnoses, prescription data
Secrets: API keys, passwords, tokens, certificates
Intellectual property: source code, business documents

Data exposure analysis:

Who/what has access to sensitive data stores?
Is the data encrypted at rest and in transit?
Is the data publicly accessible?
Is access logged and audited?

AI-SPM (AI Security Posture Management)

AI workload discovery:

Discovers AI/ML assets: SageMaker endpoints, Azure OpenAI deployments, Vertex AI models, Bedrock, custom model servers
Identifies AI model training data locations
Detects AI inference API exposure

AI-specific risks:

Exposed AI API endpoints without authentication
Training data in publicly accessible storage
Model artifacts accessible to unauthorized identities
AI workloads with overprivileged IAM roles

Shift-Left / CI/CD Integration

Image scanning in CI/CD:

Orca CLI (orca-cli) for pipeline integration
GitHub Actions, GitLab CI, Jenkins, CircleCI integrations
Block pipelines on policy violations (configurable severity thresholds)
SARIF output for GitHub Code Scanning

IaC scanning:

Terraform, CloudFormation, ARM templates, Helm charts, Kubernetes YAML
Pre-deployment misconfiguration detection
Policy-as-code enforcement in CI/CD

Registry scanning:

Scheduled scans of container registries
New image detection triggers automatic scan
Registry compliance policies (block non-compliant images from being pulled)

Integrations

SIEM/SOAR:

Splunk (webhook or Splunk app)
Microsoft Sentinel (via webhook or native connector)
Sumo Logic, Datadog
Generic webhook for custom SOAR platforms

Ticketing:

Jira, ServiceNow, PagerDuty
Configurable routing rules (which alert → which project/queue)
Two-way sync for alert resolution

Notification:

Slack, Teams, email, OpsGenie

API access:

REST API for programmatic access to findings, assets, risk scores
API key authentication
Webhook subscriptions for real-time event delivery

Common Operational Patterns

Tuning Alert Volume

Orca often surfaces thousands of findings on initial deployment. Recommended triage approach:

Focus on critical attack paths first — Use Orca's attack path view, not the flat alert list
Filter by environment — Tag production resources; prioritize prod over dev
Filter by internet exposure — Internet-exposed assets with critical findings first
Use risk score threshold — Start with risk score > 70 as the initial actionable set
Create suppression rules for:
- Known-good configurations (documented exceptions)
- Dev/sandbox accounts with different risk tolerance
- Specific resource types that are not relevant

Measuring Posture Improvement

Track over time:

Risk score distribution (count of critical/high/medium findings)
Compliance framework scores per account
Mean time to remediate (MTTR) by severity
Attack path count over time
Vulnerabilities by age (time open)

Account Onboarding

AWS onboarding:

In Orca console: Connect Cloud Account → AWS
Deploy CloudFormation stack (creates IAM role with required permissions)
Orca validates permissions and starts scanning
Initial scan completes in hours depending on account size

Azure onboarding:

Connect Cloud Account → Azure
Run provided PowerShell script (creates App Registration + role assignments)
Orca validates and begins scanning subscriptions

Permissions required:

AWS: SecurityAudit + ReadOnlyAccess + specific snapshot permissions
Azure: Reader + additional read roles for specific services
GCP: Custom role with viewer permissions across required APIs

Reference Files

Load this when you need deep architectural knowledge:

references/vulnerability-management.md -- Vulnerability-management-specific angle: Sonar Query Language examples, CNAPP capabilities, and an Orca-vs-Wiz-vs-Prisma-Cloud comparison. Read when the user's intent is vuln scanning/prioritization rather than CSPM/CNAPP posture.

orca

Files2

SKILL.mdplugins/security/skills/orca/SKILL.md