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Introduction:
While AI agents are rapidly evolving, they lack the nuanced, hands-on skills of a junior security analyst—knowing which Volatility3 plugin to run on a suspicious memory dump, which Sigma rules catch Kerberoasting, and how to scope a cloud breach across three providers. To truly augment or replace human decision-making, AI must be trained with these exact technical workflows. This article bridges that gap by extracting actionable techniques from the latest cybersecurity research (repo: https://lnkd.in/dMzkKZvz) and providing step‑by‑step guides, commands, and configurations for memory forensics, detection engineering, and multi‑cloud incident scoping.
Learning Objectives:
- Execute Volatility3 plugins to analyze Windows memory dumps for hidden processes, malicious commands, and injected code.
- Write and convert Sigma rules to detect Kerberoasting attacks (event ID 4769 with RC4 encryption) across SIEM platforms.
- Scope a cloud breach across AWS, Azure, and GCP using native CLI tools and audit logs.
You Should Know:
- Memory Forensics with Volatility3 – From Plugin Selection to Malware Discovery
Volatility3 is the next‑generation memory forensics framework that eliminates the need for profile guessing. A junior analyst knows to start with `windows.psscan` to list hidden processes, then `windows.cmdline` to capture executed commands, and `windows.malfind` to detect injected code. Here’s how to do it on Linux or Windows.
Step‑by‑step guide:
1. Install Volatility3 (Python 3.8+ required):
- Linux/macOS: `git clone https://github.com/volatilityfoundation/volatility3.git && cd volatility3 && python3 -m pip install -r requirements.txt`
– Windows: Download the zip or use `pip install volatility3` (ensure Python is in PATH).
2. Verify installation: `python3 vol.py -h`
3. List available plugins: `python3 vol.py –list-plugins`
4. Analyze a memory dump (example `memory.dmp`):
- Find hidden processes: `python3 vol.py -f memory.dmp windows.psscan`
– Capture command lines: `python3 vol.py -f memory.dmp windows.cmdline.CmdLine`
– Detect injected code regions: `python3 vol.py -f memory.dmp windows.malfind.Malfind`
- Investigate network artifacts: `python3 vol.py -f memory.dmp windows.netscan`
What this does: These commands extract process objects from memory, reveal arguments passed to malware (e.g., Mimikatz), and identify code injections that evade live AV. For AI agents, feeding these outputs as structured JSON (
–output=json) enables automated triage. -
Sigma Rules for Kerberoasting – Detection Engineering for AD Attacks
Sigma is a generic, open‑source signature format for SIEM systems. Kerberoasting (attacking service accounts with RC4 encryption) generates Windows event ID 4769 (Kerberos service ticket request) with Ticket Encryption Type 0x17 (RC4). A Sigma rule catches this.
Step‑by‑step guide to create and convert a Sigma rule:
1. Write a Sigma rule (save as `kerberoasting_rc4.yml`):
title: Kerberoasting Activity (RC4 Encryption) status: experimental description: Detects TGS requests with RC4 encryption (0x17) for service accounts logsource: product: windows service: security detection: selection: EventID: 4769 TicketEncryptionType: 0x17 ServiceName: '$' Service accounts end with $ condition: selection falsepositives: - Legacy applications using RC4 level: high
2. Install Sigmac (conversion tool): `pip install sigmatools`
3. Convert to your SIEM:
- For Splunk: `sigmac -t splunk kerberoasting_rc4.yml > kerberoasting_splunk.conf`
– For Elasticsearch: `sigmac -t elasticsearch kerberoasting_rc4.yml > kerberoasting_elastic.json`
– For QRadar: `sigmac -t qradar kerberoasting_rc4.yml > kerberoasting_qradar.rules`
- Test the detection: Simulate Kerberoasting using `Rubeus.exe` or
GetUserSPNs.py:
– Linux: `python3 GetUserSPNs.py -request -dc-ip 10.0.0.1 domain.com/user`
– Windows: `Rubeus.exe kerberoast /rc4opsec`
Why AI needs this: Without Sigma rules, an AI agent cannot correlate raw Windows event logs to adversarial behavior. Converted rules give AI structured detection logic that can be fed into LLM‑based SOC assistants.
- Scoping a Cloud Breach Across AWS, Azure, and GCP
A junior analyst must quickly determine the blast radius across multiple providers. Use CLI tools to audit IAM, storage, and compute resources.
AWS – Find unauthorized access:
- Enable CloudTrail: `aws cloudtrail create-trail –name security-trail –s3-bucket-name my-bucket`
– Search for suspicious `AssumeRole` orConsoleLogin: `aws cloudtrail lookup-events –lookup-attributes AttributeKey=EventName,AttributeValue=AssumeRole –start-time “2025-01-01T00:00:00Z”`
– List all S3 buckets with public access: `aws s3api list-buckets | jq ‘.Buckets[].Name’ | xargs -I {} aws s3api get-bucket-acl –bucket {}`
Azure – Investigate activity logs:
- Install Azure CLI: `az login`
– Query sign‑ins from unusual locations: `az monitor activity-log list –query “[?contains(operationName.value, ‘login’) && properties.riskLevel == ‘high’]”`
– List all storage accounts and their network rules: `az storage account list –query “[].{name:name, defaultAction:networkRuleSet.defaultAction}”`
GCP – Scope compromised service accounts:
- Install `gcloud` and set project: `gcloud config set project my-project`
– Pull Cloud Audit Logs for token generation: `gcloud logging read “protoPayload.methodName=’GenerateAccessToken’ AND protoPayload.authenticationInfo.principalEmail:[email protected]”`
– Identify all resources attached to a compromised service account: `gcloud projects get-iam-policy my-project –flatten=”bindings[].members” –filter=”bindings.members:serviceAccount:[email protected]”`Scoping workflow for AI agents: Automate these CLI queries into a Python script that collects evidence across three providers, normalizes timestamps, and outputs a unified incident timeline.
- Training AI Agents with These Skills (RAG & Prompt Engineering)
To make an AI agent understand memory dumps, Sigma rules, and cloud logs, you must build a Retrieval‑Augmented Generation (RAG) pipeline or hardcode skill definitions.
Example prompt structure for an AI incident responder:
You are a SOC analyst. Given a memory dump hash, run: - Volatility3 windows.psscan - windows.malfind - windows.netscan Convert the JSON output into a summary of malicious processes and network connections. Then, check for Kerberoasting by querying Windows event logs with Sigma rule "kerberoasting_rc4.yml". Finally, if cloud access keys are found, run AWS/Azure/GCP scope commands.
Why this matters: Without this explicit instruction, a generic AI (like GPT‑4) cannot execute or interpret forensic tools. By embedding command syntax and decision trees, you transform a language model into a semi‑autonomous analyst.
5. Practical Lab – Putting It All Together
Set up a home lab to practice:
- Generate a memory dump: Use `DumpIt.exe` or `LiME` on a Windows VM after running Kerberoasting.
- Analyze with Volatility3 as in Section 1.
- Trigger Sigma detection by forwarding Windows event logs to an Elastic Stack and importing the rule.
- Simulate a cloud breach: Use a free tier AWS account, create a test IAM user, and perform unauthorized `s3:GetObject` from a new IP, then scope using the CLI commands above.
6. Automating the Workflow with Python
Save time by scripting the repetitive parts:
import subprocess, json
Run Volatility3 and parse output
result = subprocess.run(['python3', 'vol.py', '-f', 'memory.dmp', 'windows.psscan', '--output=json'], capture_output=True)
processes = json.loads(result.stdout)
for proc in processes:
if 'suspicious.exe' in proc['ImageFileName']:
print(f"Alert: {proc['ImageFileName']} PID {proc['PID']}")
For cloud, use `boto3` (AWS), `azure-identity` (Azure), and `google-cloud-logging` (GCP) to programmatically fetch audit logs.
What Undercode Say:
- Key Takeaway 1: AI agents cannot replace a junior analyst’s tool‑specific knowledge unless explicitly trained with command‑level workflows – Volatility3 plugins, Sigma rule syntax, and cloud CLI queries are non‑negotiable skills.
- Key Takeaway 2: The repo (https://lnkd.in/dMzkKZvz) likely contains a curated skill set for AI‑powered incident response; integrating these into a RAG pipeline will produce a far more capable defender than generic LLMs.
- Analysis: The cybersecurity industry is moving toward hybrid human‑AI teams. However, the “black box” nature of AI means that without transparent, verifiable commands (like the ones above), organizations will not trust AI to handle memory forensics or cloud breach scoping. The future belongs to those who can translate expert human procedures into machine‑executable playbooks.
Prediction:
Within 18 months, AI agents will autonomously run Volatility3 on memory dumps, convert Sigma rules to native SIEM queries, and correlate multi‑cloud breach indicators—all without human prompts. However, this will require a new class of “AI incident response engineer” who validates outputs and updates tool signatures. The gap between a junior analyst’s intuition and an AI’s deterministic execution will close, but only for organizations that invest in structured, command‑based training datasets today. Expect the first fully AI‑led breach scoping to happen by late 2026, followed by heated debates on liability and false positives.
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Reported By: Syed Muneeb – Hackers Feeds
Extra Hub: Undercode MoN
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