What is attack path analysis in cloud security?

Attack path analysis maps how an attacker could move from an entry point (like the internet) through cloud assets, identities, and vulnerabilities to reach sensitive data or privileged access.

What is a security graph in cloud security?

A security graph models cloud resources, identities, network paths, and findings as connected nodes and edges, enabling queries like which critical CVEs sit on paths to production databases.

How is attack path analysis different from vulnerability scanning?

Vulnerability scanning lists flaws on individual assets. Attack path analysis asks whether those flaws are reachable and what an attacker could access after exploiting them.

Attack Path Analysis in Cloud Security: A Practical Guide

Attack path analysis answers the question every cloud security team eventually asks: If an attacker starts from the internet, what can they actually reach?

Traditional tools report thousands of misconfigurations and CVEs. Attack path analysis connects those signals into chains — showing which weaknesses sit on realistic routes to crown jewels. Combined with a security graph, it turns cloud security from alert triage into risk navigation.

What is a cloud attack path?

A cloud attack path is a sequence of steps an adversary could take through your environment:

Entry — internet-facing load balancer, exposed API, compromised credential
Movement — lateral hops via network, IAM role assumption, container escape
Objective — sensitive datastore, admin console, secrets vault

Each step is a node; permissions and reachability are edges. A critical CVE on an internal-only dev box is a dead end. The same CVE on an internet-exposed VM with a role that can read production S3 is a live attack path.

Why this is not a CVSS sort

A critical CVE on an isolated lab VM is a dead end; a medium misconfiguration on an internet-facing role that can read production data is a live path. How to run that ranking every week is how to prioritize cloud vulnerabilities. This page is the model: nodes, edges, and queries.

How a security graph powers attack path analysis

A security graph cloud teams can query looks like this:

Node types	Examples
Entry	Internet, VPN, partner network
Workloads	EC2, Lambda, GKE pods, Azure VMs
Identity	IAM roles, service accounts, RBAC bindings
Data	S3, RDS, Azure Storage, GCS buckets
Findings	CVEs, misconfigurations, secrets

Edge types	Meaning
`REACHABLE`	Network path exists
`ASSUMES` / `CAN_ASSUME`	Identity chain
`AFFECTS`	Vulnerability on asset
`CAN_ACCESS`	Permission to read/write resource

Example query a graph engine runs:

Show critical CVEs on workloads reachable from the internet within 4 hops of a production datastore.

That is attack path analysis — not a sort by CVSS column.

Attack path analysis vs. other cloud security approaches

Approach	What it shows	Limitation
CSPM	Misconfigurations	Findings in isolation
Vulnerability scanner	CVEs per host	No path context
CIEM	Over-privileged identities	May lack network reachability
Attack path analysis	End-to-end exploit chains	Requires unified graph model

Modern CNAPP platforms converged on graphs because buyers needed this layer. Open-source projects like OpenSourceOM apply the same model with self-hosted, auditable code.

Real-world attack path examples

Example 1: Internet to data exfiltration

Internet → ALB → EC2 (CVE-2024-xxxx) → IAM Role → S3 (customer PII)

Fix priority: patch or isolate the EC2 and restrict the role — either break breaks the path.

Example 2: Identity-only path

Phished developer credential → IAM user → AssumeRole → Admin → All accounts

No CVE required. CIEM + graph identifies the role chain as the critical control.

Example 3: False alarm deprioritization

Critical CVE → isolated dev VM → no outbound to prod

Attack path analysis deprioritizes this despite high CVSS — no path to objective.

Implementing attack path analysis

Step 1: Unified inventory

Ingest AWS, Azure, GCP resources, Kubernetes, and IAM into one schema. Without normalization, graphs fragment by provider.

Step 2: Model reachability

Compute network paths (security groups, NACLs, peering, ingress rules) and identity paths (trust policies, RBAC).

Step 3: Attach findings

Enrich nodes with CVEs, misconfigurations, and secrets from scanners and CSPM.

Step 4: Query and visualize

Security engineers ask path questions; developers receive findings with path context in tickets.

Step 5: Continuous sync

Cloud drift invalidates yesterday’s paths. Collectors must run incrementally.

Read the technical overview: The security graph.

Open-source attack path analysis

Teams choose self-hosted security graph platforms when they need:

Data residency in their VPC
Transparent path logic (no black-box scores)
Custom connectors for internal systems
Apache-2.0 extensibility

OpenSourceOM Core targets this stack: collectors → graph store → path queries → prioritized findings.

Key takeaways

Attack path analysis maps exploitable routes, not isolated alerts
A security graph cloud model connects workloads, identities, data, and findings
Cloud attack path prioritization beats CVSS-only sorting in dynamic environments
CNAPP and open-source graph platforms share this architecture
Start with inventory + reachability; add findings enrichment second