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Introduction:
As hybrid and multi-cloud environments grow, identity has become the new security perimeter. However, most traditional security monitoring tools fail to see how permissions and group memberships create indirect, dangerous connections between identities and critical assets. The new ISO-standard Graph Query Language (GQL), combined with Microsoft Sentinel’s Identity Attack Graph, changes this by letting security teams visually map and query every possible lateral movement path an attacker could take, transforming threat hunting from reactive alert-chasing into proactive risk elimination.
Learning Objectives:
- Understand the core components and architecture of the Identity Attack Graph in Microsoft Sentinel.
- Write and execute GQL queries to uncover hidden privilege escalation and lateral movement paths.
- Apply preventive controls and cloud hardening commands based on attack graph findings.
- Graph Query Language (GQL) and the Identity Attack Graph
What are they and why they matter
Graph Query Language (GQL) is an ISO/IEC 39075:2024 standard that provides a unified way to query property graphs—structures that model entities (nodes) and their relationships (edges). Unlike SQL, which struggles with many-to-many relationships and indirect connections, GQL excels at traversing paths of relationships, making it ideal for security analysis.
Microsoft Sentinel’s Identity Attack Graph uses this GQL engine to automatically build a relationship map of your environment. It ingests data from Microsoft Entra ID (users, groups, service principals) and Azure Resource Graph (subscriptions, VMs, storage accounts, Key Vaults) to model how identities connect to resources through role assignments and group memberships.
Step‑by‑step guide to enable the Identity Attack Graph:
- Ensure prerequisites: Microsoft Sentinel data lake enabled, Microsoft Entra ID Assets and Azure Resource Graph connectors installed.
- Enable the graph in Microsoft Defender portal → Microsoft Sentinel → Graphs → Identity attack graph card → “Set up graph”.
- Verify connectivity from your analyst workstation using Azure CLI (Linux/macOS):
az login --tenant YOUR_TENANT_ID az rest --method get --url "https://api.security.microsoft.com/api/identity/attackgraph/config"
PowerShell (Windows):
Connect-AzAccount -Tenant YOUR_TENANT_ID
Invoke-RestMethod -Uri "https://api.security.microsoft.com/api/identity/attackgraph/config" -Headers @{Authorization = "Bearer $((Get-AzAccessToken).Token)"}
Wait for up to 48 hours for initial data ingestion.
- Writing Your First GQL Queries for Attack Path Discovery
Core GQL syntax patterns
GQL queries start with a `MATCH` clause that describes the pattern of nodes and edges you want to find. Basic pattern without variables:
MATCH ()-[]-() RETURN COUNT()
This counts all relationships in the graph. To access node properties, use variables:
MATCH (person:User)-[e:CanRead]->(target:KeyVault) RETURN person.name, target.name, e.type
This returns every user who can read a Key Vault, and the type of permission edge that enables it.
Finding lateral movement paths to sensitive secrets
To list all attack paths from any identity to a Key Vault containing “prod-secrets”:
MATCH (source:Identity)-[bash]->(target:Resource) WHERE target.type == "Microsoft.KeyVault/vaults" AND target.name CONTAINS "prod-secrets" RETURN source.identityName, target.name, permission.type
This query directly surfaces the exact permissions and edges that an attacker could exploit to pivot from a low‑privilege account to your most sensitive secrets.
- Identifying Overprivileged Accounts and Shortest Paths to Critical Assets
Querying for privilege escalation chains
One of the most powerful uses of the Identity Attack Graph is to find indirect paths to privileged roles. The following GQL query identifies all identities that have a path—direct or through group memberships—to the “Global Administrator” role:
MATCH (source:Identity)-[:MemberOf1..5]->(group:Group)-[:AssignedRole]->(role:Role) WHERE role.name == "Global Administrator" RETURN source.name, group.name, role.name
This uses variable‑length path matching (1..5) to traverse up to five group membership edges. The result shows you which seemingly non‑privileged users actually have hidden paths to the highest‑level role.
Focusing on shortest paths for remediation
To prioritize fixes, you can return only the shortest path to a critical asset:
MATCH p = (source:Identity)-[]->(target:Resource) WHERE target.name == "critical-sql-database" RETURN p, length(p) AS pathLength ORDER BY pathLength ASC LIMIT 10
This orders attack paths by length, helping you first eliminate the shortest, most direct risks.
- From Query to Hardening: Linux and Windows Commands to Break Attack Paths
Pruning excessive permissions (Azure CLI)
Once you identify an overprivileged account or group via GQL, remove the risky role assignment:
az role assignment delete --assignee "[email protected]" --role "Contributor" --scope "/subscriptions/..."
Implementing Just‑In‑Time (JIT) access
For accounts that require occasional privileged access, configure JIT to eliminate standing permissions:
az security jit-policy create --location "westus" --resource-group "myRG" --vm "myVM" --port "22" --protocol "TCP" --allowed-source-address-prefixes "" --max-request-duration "PT3H"
Windows PowerShell: Auditing and breaking lateral movement paths
To locally audit Kerberos delegation (a common lateral movement vector):
Get-ADUser -Filter {TrustedForDelegation -eq $true} | Select-Object Name, SamAccountName
Disable unnecessary unconstrained delegation:
Set-ADUser -Identity "username" -TrustedForDelegation $false
Linux: Removing dangerous sudo rights
If a GQL query shows a Linux VM can be accessed by an overly broad group, audit sudoers:
grep -r "^[^].ALL=(ALL:ALL) ALL" /etc/sudoers /etc/sudoers.d/
Then comment out any overly permissive lines.
- Integrating GQL with SIEM and Cloud Hardening
Combining GQL with KQL for enrichment
GQL queries can be combined with Kusto Query Language (KQL) to enrich graph findings with real‑time telemetry. For example, after finding a risky path, you can check for actual exploitation attempts:
IdentityLogonEvents | where AccountUpn == "[email protected]" | where Timestamp > ago(7d) | summarize FailedLogins = countif(ErrorCode != "0"), SuccessfulLogins = countif(ErrorCode == "0") by AccountUpn
This gives you context on whether a risky identity has shown signs of compromise.
Cloud hardening checklist based on attack graph findings
- Remove direct role assignments to users; use groups instead.
- Enable Conditional Access policies for all high‑risk paths identified.
- Use Azure Privileged Identity Management (PIM) for just‑in‑time approvals.
- Regularly re‑run GQL queries to track changes in attack paths after remediations.
What Undercode Say
- Key Takeaway 1: GQL transforms security from reactive log analysis to proactive path mapping, revealing attack chains that were previously invisible to traditional tools.
- Key Takeaway 2: The Identity Attack Graph’s ability to answer “What resources could an attacker reach if this account is compromised?” shifts the focus from who has access to how they can move—fundamentally changing cloud defense strategies.
+ Analysis
The ISO standardization of GQL is a milestone comparable to SQL’s standardization in the 1980s. By adopting a unified query language, Microsoft Sentinel now offers defenders a consistent way to hunt across identity and resource relationships without vendor lock‑in. This is not just a feature update—it is a paradigm shift. SOC teams can finally move from manually stitching together permissions across dozens of logs to asking complex, path‑based questions in plain, graph‑aware syntax. The result is a dramatic reduction in mean time to detect (MTTD) for identity‑based attacks and a clear path toward preventive security posture management.
Prediction
Over the next 18–24 months, GQL will become a standard skill for cloud security engineers, much like SQL is for database administrators. As more SIEM and XDR platforms adopt the ISO standard, threat hunting will shift from signature‑based alerts to graph‑based anomaly detection. Microsoft’s early integration of GQL into Sentinel and Defender XDR signals that future security tools will be built around relationship intelligence, not just log aggregation. Organizations that train their teams on GQL now will have a significant advantage, while those that rely solely on legacy SIEMs will increasingly miss the complex, multi‑hop attacks that characterize modern identity breaches.
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Reported By: Mariocuomo Microsoftsentinel – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
