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Introduction:
The security industry is currently mesmerized by the promise of “Agentic SOCs,” where autonomous AI agents triage alerts, hunt for threats, and even initiate remediation. However, the true, enduring competitive advantage in this new paradigm won’t be won by the agents themselves, but by the invisible yet foundational platform layer beneath them. As Krishna Kumar Parthasarathy, CVP at Microsoft Security, defines it, this foundation is the “substrate”—a purpose-built layer of a data lake, a security graph, and contract-based tools that together give an agent the ability to reason, see, and act effectively. This article moves beyond the hype of agentic announcements to deconstruct the technical architecture of this substrate, providing a hands-on guide for security professionals to build, test, and understand the components that will define the future of autonomous security operations.
Learning Objectives:
- Objective 1: Understand the three critical components of the agentic “substrate” and why they are inseparable for building reliable security agents.
- Objective 2: Learn how to query and interact with a security graph to provide “vision” for an agent, using both Microsoft’s graph API and other open-source methodologies.
- Objective 3: Gain hands-on experience in building and hardening a secure data lake “memory” for agents, implementing command-line tools to simulate agent memory operations, and applying safe development guardrails.
You Should Know:
1. The Substrate’s Triad: Memory, Vision, and Hands
Krishna Kumar’s core thesis is that an agentic SOC is won at the “platform layer” by three integrated components: a data lake for an agent’s memory, a security graph for an agent’s vision, and contract-defined tools for an agent’s hands. This triad is the “substrate” that allows an agent to understand its environment and take reliable action. Today, we see this architecture being built across the industry, from Microsoft’s Azure Data Lake and Security Graph to open-source projects and platforms from competitors like Huawei, Netskope, and Arctic Wolf. The key takeaway is that the value is not in the agent but in the data and relationships it can access.
To help security teams understand this, the open-source AI-SOC-Agent project on GitHub provides a powerful, real-world example. This CLI-based autonomous agent uses natural language to generate Kusto Query Language (KQL) for threat hunting in Azure Sentinel, then performs automated remediation with strict guardrails. Let’s explore its “substrate” by examining its memory and tooling. First, to simulate the “data lake memory” component, we can use basic command-line tools to show how an agent might access a structured data store.
Step‑by‑Step Guide: Simulating an Agent’s Data Lake Memory Access
This process demonstrates how an agent might index and retrieve data from a local or network share, acting as a simple “memory” store.
- Create a Test Data Lake Directory: On a Linux machine, create a directory to act as your simulated data lake, and generate some sample log files.
Create the data lake directory mkdir -p ~/agentic_datalake/logs Generate sample security logs (e.g., from a simulated application) for i in {1..5}; do echo "Timestamp: $(date), Event: Login from IP 10.0.0.$i, Status: Success" >> ~/agentic_datalake/logs/auth.log; done for i in {1..3}; do echo "Timestamp: $(date), Event: Failed login from IP 192.168.1.$i, Status: Failure" >> ~/agentic_datalake/logs/auth.log; done - Implement a Simple Agent Memory Index: Create a `memory_index.txt` file that an agent could use as a quick lookup reference.
Create a simple index find ~/agentic_datalake -type f -name ".log" > ~/agentic_datalake/memory_index.txt
- Query the Agent’s Memory: Simulate an agent’s data retrieval using standard Linux utilities.
Simulate a "hunt" for failed logins in the agent's memory grep "Failed login" ~/agentic_datalake/logs/auth.log
This command would return lines like
Timestamp:..., Event: Failed login from IP 192.168.1.1, Status: Failure, demonstrating how an agent could quickly fetch specific data from its structured “memory.”
2. Providing Vision: Querying the Security Graph
If the data lake is an agent’s memory, the Security Graph is its vision, allowing it to see complex relationships between users, devices, IP addresses, and alerts that flat logs cannot. Microsoft’s Intelligent Security Graph, for example, uses advanced graph analytics to uncover hidden attack patterns. This is realized through products like the Microsoft Graph Security API and within Microsoft Sentinel’s graph capabilities for incident correlation. An agent with access to this graph can query entities like `UserA` and discover every related Device, IP Address, and Alert, instantly providing context that would take a human hours to assemble.
Step‑by‑Step Guide: Interacting with a Security Graph
This guide demonstrates using the Microsoft Graph Security API and a KQL graph query to give an agent “vision.”
Option A: Using Microsoft Graph Security API
This API provides a unified interface to alerts across Microsoft and partner security solutions. An agent can use it to ingest alerts as graph data.
1. Prerequisites: Register an application in Azure AD and grant it the `SecurityAlert.Read.All` application permission.
2. Get an Access Token: Use the client credentials flow to obtain a token.
PowerShell example to get a token. Replace tenant-id, client-id, and client-secret.
$tenantId = "your-tenant-id"
$clientId = "your-client-id"
$clientSecret = "your-client-secret"
$resource = "https://graph.microsoft.com"
$body = @{
grant_type = "client_credentials"
client_id = $clientId
client_secret = $clientSecret
scope = "https://graph.microsoft.com/.default"
}
$response = Invoke-RestMethod -Method Post -Uri "https://login.microsoftonline.com/$tenantId/oauth2/v2.0/token" -ContentType "application/x-www-form-urlencoded" -Body $body
$token = $response.access_token
3. Query for Alerts: The agent can now call the API to get a graph of security alerts.
$headers = @{ Authorization = "Bearer $token" }
$alerts = Invoke-RestMethod -Method Get -Uri "https://graph.microsoft.com/v1.0/security/alerts_v2" -Headers $headers
The agent can now parse $alerts.value for incident correlation
$alerts.value | Format-Table -Property id, title, severity, status, category
4. Filter by Status: The agent can refine the query to find active, high-severity incidents for prioritization.
$activeAlerts = $alerts.value | Where-Object { $<em>.status -eq "new" -and $</em>.severity -eq "high" }
Option B: Using KQL Graph Semantics (within Microsoft Sentinel)
Microsoft’s Kusto Query Language (KQL) now includes graph semantics, allowing analysts and agents to directly query relationships in the data lake.
1. Navigate to Microsoft Sentinel: In the Azure portal, go to your Microsoft Sentinel workspace.
2. Select “Logs”: Open the Logs section to run KQL queries.
3. Run a Graph Query: This query identifies relationships between a specific user and their logon activity from non-corporate IPs.
// Define the source table (e.g., SigninLogs from Microsoft Entra ID) let user_id = "[email protected]"; SigninLogs | where UserPrincipalName == user_id | where isnotempty(IPAddress) // Use graph semantics to show relationships between the user and IPs | make-graph source on UserPrincipalName --> IPAddress // Filter for logins from IPs that are not in the corporate range | where IPAddress !in ("192.168.0.0/16", "10.0.0.0/8") | project UserPrincipalName, IPAddress, City, Country, RiskLevel
This query, powered by built-in KQL graph semantics, allows an agent to see the “edges” between a user entity and various IP nodes, a powerful form of ‘vision’.
3. The Agent’s Hands: Contract-Based Tools and Actions
A security agent’s ability to take action (its “hands”) must be strictly defined through tools shaped as contracts. This prevents a bad prompt from executing a catastrophic command. This is often implemented as a set of pre-approved, callable functions or plugins. For example, Microsoft Security Copilot agents use plugins to extend their capabilities to non-Microsoft services via APIs, and the AI-SOC-Agent project includes automated “SOAR workflows” like isolating a VM as a defined, safe action.
Step‑by‑Step Guide: Defining a Safe Tool Contract for Agent Remediation
This guide uses Python to create a simple, contract-based tool that an agent could use to trigger a secure workflow, such as disabling a user account.
- Define the Function (
user_action_contract.py): Create a Python script that defines the action as a function with a clear name and defined parameters.import subprocess import sys Contract: This function is the ONLY way an agent can disable a user def safe_disable_user_contract(username_to_disable): """ SAFE_CONTRACT: Disables a user account (e.g., in Active Directory). This function requires explicit authorization and will log all actions. """ print(f"[bash] Agent action requested to disable user: {username_to_disable}") Implement a safe version for local user management (for testing) WARNING: This is a simulation. In production, integrate with your IAM system. try: Example using 'usermod' on Linux to lock a local user account. In a real SOC, this would call an API to Azure AD or your IdP. result = subprocess.run(['sudo', 'usermod', '-L', username_to_disable], capture_output=True, text=True, check=False) if result.returncode == 0: print(f"[bash] User {username_to_disable} was successfully disabled.") Log the action to a secure, immutable audit log. with open("/var/log/agent_audit.log", "a") as log_file: log_file.write(f"Agent disabled user: {username_to_disable}\n") else: print(f"[bash] Failed to disable user {username_to_disable}. Error: {result.stderr}", file=sys.stderr) except Exception as e: print(f"[bash] Exception during user disable: {e}", file=sys.stderr) If the script is run directly, simulate an agent making a request. if <strong>name</strong> == "<strong>main</strong>": The agent can only call this one function. safe_disable_user_contract("test_user") - Create a Test User (Linux): For demonstration purposes, create a test user to disable.
sudo useradd test_user
- Run the Contracted Tool: Execute the Python script from the command line. You will likely need `sudo` permissions to run the `usermod` command.
sudo python3 user_action_contract.py
The output will show the success message, and you can verify the user is locked by checking the shadow file:
sudo grep test_user /etc/shadow
(An exclamation mark `!` in the password field indicates the account is locked). This simple example shows how an agent’s “hands” are constrained to a specific, safe, and auditable function.
What Undercode Say:
- Key Takeaway 1: The competition in agentic SOCs is not about building the smartest AI agent, but about building the richest, most secure, and most reliable “substrate” underneath it. Agents are interchangeable; data and relationships are strategic assets.
- Key Takeaway 2: A secure agentic future requires “defense in depth” applied to the agent itself. This means immutable audit logs for every action, strict API contracts for remediation, and automated testing of agent behavior before deployment, as seen in Arctic Wolf’s approach where new agents must outperform human workflows in a sandbox before being released to production.
Analysis:
The concept of the “substrate” shifts the entire security operations value chain. For years, the focus was on collecting more data (SIEM) and writing better detection rules (detection engineering). The agentic SOC renders those individual efforts table stakes. The new battleground is how well that data is organized for an AI’s “memory” (data lakes with a purpose-built schema) and how comprehensively those relationships are mapped for an AI’s “vision” (graph databases like Neo4j, which have shown to reduce pipeline costs by over 90% and improve query latency by orders of magnitude compared to traditional architectures).
For security practitioners, this means upskilling is critical. The ability to structure a data lake with tools like Microsoft Fabric’s OneLake, to write KQL graph queries, and to define safe API contracts will be the new core competencies of SOC architects and engineers. The “you build it, you run it” mantra is being extended to “you build the data model, you enable the agent.” The agent itself is merely the consumer of that well-defined substrate.
Prediction:
Within the next 12 to 18 months, the market will realize that standalone AI agents are a commodity. The real winners will be security platforms that have invested heavily in their underlying graph and data lake substrate. We will see the first major security vendor “win” an agentic SOC deployment not because its agent is smarter, but because its graph was deeper and its data lake was more responsive and secure. Consequently, we will also witness the first high-profile security failure caused by an agent acting on poorly structured or misleading data (a new form of “data poisoning” on an agent’s memory), leading to a new class of “supply chain” attacks targeting the substrate itself. Defenses for the substrate will, ironically, become the next major security market.
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Reported By: Harish Aitharaju – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
