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Introduction:
In a recent viral post, Adam Biddlecombe highlighted a stark reality: most people use less than 10% of Excel’s capabilities. While this statistic is often aimed at business productivity, in the cybersecurity domain, it serves as a chilling metaphor. Security Operations Centers (SOCs) and IT teams frequently operate at a similar capacity regarding their own enterprise tools—SIEMs, EDRs, and Cloud Security Posture Management (CSPM) platforms. The gap between a tool’s capability and an analyst’s proficiency is exactly the space threat actors exploit. This article transforms that “10% problem” into a hard-hitting curriculum, moving from basic data hygiene to advanced security analytics, ensuring you utilize 100% of your available telemetry to detect and mitigate breaches before they become front-page news.
Learning Objectives:
- Master advanced query logic (KQL, SPL) to hunt for adversarial behaviors rather than just static IOCs.
- Implement automated threat feed integration and enrichment pipelines using Python and REST APIs.
- Configure cloud-1ative hardening controls (AWS/Azure) to address the top 5 misconfigurations leading to data exfiltration.
You Should Know:
1. The “Excel Mentality” in SIEM Optimization
Most SOC analysts treat their SIEM like a spreadsheet: a place to dump logs and run basic “CTRL+F” searches for known malicious IPs. This is the security equivalent of using Excel for basic addition while ignoring pivot tables and macros. To break this cycle, you must understand that your SIEM is a relationship database. It’s not about what happened, but what changed.
Step‑by‑step: Baselining Normal Behavior
- Step 1: Identify your “Crown Jewels” (Domain Controllers, Financial DBs).
- Step 2: Run a 30-day historical query to establish a baseline for failed login attempts. For Splunk:
index=windows EventCode=4625 | stats count by user, source_ip | where count > 10. - Step 3: Configure a threshold-based alert that triggers only when the current 5-minute window deviates from the baseline by 3 standard deviations.
2. Linux Command-Line Forensics: Beyond `grep`
If you are only using `grep` to search logs, you are using 10% of Linux’s forensic power. We need to pivot to constructing pipelines that correlate user activity with process creation.
Step‑by‑step: Process Tree Analysis
- Step 1: To identify a potential reverse shell, we look for interactive shells spawned by web servers. Command:
sudo ausearch -ts today -m execve -k webserver. - Step 2: Pipe this to `jq` (if logs are JSON) to extract PPID (Parent Process ID) and PID.
- Step 3: Reconstruct the process tree:
ps -ef --forest | grep -C 5</code>.</li> <li>Tutorial: To see live network connections tied to processes, use `ss -tulpn` and cross-reference with <code>/proc/[bash]/cmdline</code>.</li> </ul> <h2 style="color: yellow;">3. Windows Event Log Deep Dive (PowerShell)</h2> Windows Event Logs are verbose, but most admins only look at 4624 (Logon) and 4625 (Failure). Attackers live off the land using WMI and Scheduled Tasks, which generate specific logs often ignored. <h2 style="color: yellow;">Step‑by‑step: Hunting for Lateral Movement</h2> <ul> <li>Step 1: Enable detailed tracking (Command: <code>wevtutil gl Microsoft-Windows-Sysmon/Operational</code>). Ensure Sysmon is installed with a config capturing ProcessAccess and NetworkConnect.</li> <li>Step 2: Hunt for suspicious Scheduled Tasks creation. In PowerShell: <code>Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4698} | Select-Object -Property TimeCreated, Message</code>.</li> <li>Step 3: Analyze the XML payload for encoded commands. If you see `-enc` (Base64 encoding) in the command line, extract it immediately: <code>[System.Text.Encoding]::UTF8.GetString([System.Convert]::FromBase64String("encoded_string"))</code>.</li> </ul> <h2 style="color: yellow;">4. API Security and AI Pipeline Hardening</h2> With the rise of LLMs, API keys are the new gold. The "Excel 10%" problem here is failing to implement Rate Limiting and Input Sanitization, leaving your AI prompt endpoints vulnerable to injection and denial of wallet. <h2 style="color: yellow;">Step‑by‑step: Securing a FastAPI Endpoint</h2> <ul> <li>Step 1: Implement a custom middleware to validate input size and structure.</li> <li>Step 2: Use `Pydantic` to strictly define expected JSON schemas, rejecting any unexpected fields (mass assignment protection).</li> <li>Step 3: Code snippet for Rate Limiting: [bash] from slowapi import Limiter from slowapi.util import get_remote_address limiter = Limiter(key_func=get_remote_address) @app.post("/generate") @limiter.limit("5/minute") async def generate_text(request: Request): return {"status": "ok"} - Step 4: Apply a WAF rule to block requests containing SQL or LDAP injection strings targeting your AI models.
5. Cloud Hardening: AWS S3 and IAM Misconfigurations
The 2024 Verizon DBIR shows that misconfigured cloud storage is a leading vector. Most users use the "default" permissions, which is the equivalent of using Excel's "Save As" without ever looking at the file path.
Step‑by‑step: Implementing the Principle of Least Privilege
- Step 1: Run a comprehensive audit:
aws s3api get-bucket-acl --bucket [bash]. - Step 2: Immediately block public access using the AWS CLI:
aws s3api put-public-access-block --bucket [bash] --public-access-block-configuration "BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true". - Step 3: For IAM, generate an Access Advisor report to remove unused permissions:
aws iam generate-service-last-accessed-details --arn arn:aws:iam::account-id:role/role-1ame. - Step 4: Enforce MFA on the root account via a strict policy and rotate API keys programmatically every 90 days using Lambda functions.
6. Vulnerability Exploitation (CVE-2025-XXXX) & Mitigation
Let's analyze a theoretical deserialization vulnerability in Java applications (similar to Log4shell). The exploitability comes from the ability to execute arbitrary code via untrusted data being processed by the application.
Step‑by‑step: Simulating Detection
- Step 1: Detection via WAF logs searching for `${jndi:ldap://` variants or `java.rmi.` strings in URL parameters.
- Step 2: Mitigation is not just about patching; it's about implementing a "deny list" at the network layer. Use `iptables` to block outbound LDAP/RMI traffic to the internet for internal servers:
iptables -A OUTPUT -p tcp --dport 389 -j DROP. - Step 3: For Windows, use the Windows Defender Firewall to restrict outbound connections on port 1389, 1099.
- Step 4: Patch cadence: Automate the patching of critical Java frameworks using Ansible playbooks running weekly on test environments.
7. Training The Analyst: From Data to Decision
As Adam Biddlecombe implied, the problem is literacy. Creating "Excel" style reports (static PDFs) is useless. You must build live dashboards that ask "why" rather than "what."
Step‑by‑step: Building an Interactive Threat Dashboard
- Step 1: In Elastic or Splunk, build a query that aggregates alerts by "MITRE ATT&CK Tactic."
- Step 2: Add a drill-down filter. If the tactic is "Execution," filter by "PowerShell" or "WMI."
- Step 3: Embed a live "Threat Score" using machine learning (ML) algorithms to highlight outliers.
- Step 4: Schedule a "Threat Brief" email to be sent to management, showing only the top 3 high-fidelity alerts, rather than 300 low-level logs. This improves reaction times and SOC efficiency by 40%.
What Undercode Say:
- Key Takeaway 1: Proficiency is the ultimate defense. Just as Excel has hidden functions that handle big data, SIEMs and EDRs have capabilities far beyond log aggregation. The "10% trap" creates a false sense of security, where admins assume because a tool is installed, it is working.
- Key Takeaway 2: Automation is not optional; it is mandatory. The volume of alerts requires AI-assisted triage. However, we must move beyond "alert fatigue" to "alert relevance." By fine-tuning thresholds (as seen in the SIEM baselining guide), we harness the tool's true power to reduce noise and identify the "Goldilocks" zone of security—the perfect balance of security posture and operational efficiency.
Analysis:
The core of Adam’s post resonates deeply with the security industry because it highlights a universal truth: tools are catalysts, not solutions. The difference between a major data breach and a narrowly averted incident often comes down to whether an analyst knew how to use the "LOOKUP" function on their data sources to correlate a netflow anomaly with an identity change. We are drowning in data but starving for context. By treating our security stacks as complex engines requiring constant tuning—rather than static reporting tools—we transition from reactive order-takers to proactive defenders. This requires a cultural shift in training, moving from "certification cramming" to "practical problem solving" with hands-on labs that simulate adversarial tradecraft. The best investment a company can make in 2026 is not buying a new shiny product, but unlocking the 90% potential of the products they already own.
Prediction:
- +1 Democratization of Threat Hunting: As query languages become more "AI-assisted," the barrier to entry will lower. Analysts who are literate in concepts (rather than syntax) will become the premium talent, leading to a surge in SOC efficiency.
- -1 Shadow IT Explosion: If we fail to teach teams how to properly configure permissions and monitor their custom AI/API endpoints, the "10% usage" issue will flip to "100% usage" of compromised credentials, leading to a massive uptick in business logic bypass attacks.
- +1 Embedded Training: We will see a move towards "in-tool" training modules (like command line assistants) that provide suggestions based on current telemetry, effectively pushing the industry average from 10% to 50% capability by 2028.
- -1 Complacency Hack: Attackers will specifically target the well-documented "default" settings of high-profile security tools. If you are using the default Splunk dashboard or Elastic index patterns, you are painting a bullseye on your infrastructure.
- +1 Shift to Data Hygiene: The next "Silver Bullet" will be data normalization. When organizations start treating their log schemas with the same rigor as financial spreadsheets, ML models will finally be able to detect the "0.01%" anomalies that indicate state-sponsored espionage, effectively closing the "10% gap" permanently.
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Reported By: Adam Biddlecombe - Hackers Feeds
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