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Introduction:
The cybersecurity industry is rushing to integrate Artificial Intelligence (AI) and Large Language Models (LLMs) with the explicit goal of doing “more with less.” However, historical economic theory—specifically the Jevons Paradox—dictates that increasing the efficiency of a resource does not lead to its decreased consumption, but rather to an explosion in its use. In the context of SecOps, this means that making analysts faster won’t shrink the backlog; it will validate the expansion of log sources, detection rules, and attack surface management, ensuring that the total volume of security work continues to grow exponentially.
Learning Objectives:
- Understand the Jevons Paradox and its specific implications for cybersecurity automation and AI adoption.
- Learn practical techniques for scaling SecOps pipelines using AI without creating resource bottlenecks.
- Master advanced Linux, Windows, and cloud CLI commands for automating alert triage, vulnerability scanning, and log aggregation.
You Should Know:
- The Jevons Paradox in SecOps: Why Efficiency Breeds Complexity
Ross Haleliuk’s post highlights a critical truth: AI will not eliminate the need for security professionals; it will change the scope of their work. When storage became cheap, we stopped deleting logs and started keeping everything for “hunting.” When compute became cheap, we moved from host-based AV to EDR and XDR, processing millions of events per endpoint. When AI makes the analysis of a single alert cheaper, the business will demand that every single asset and every single identity be monitored at all times.
Step‑by‑step guide explaining what this does and how to use it.
To prepare for this influx, security engineers must automate their data pipelines to accept high-volume, high-velocity data without crashing.
Step 1: Audit your current log source ingestion. Many companies ignore “verbose” logs from firewalls or OS internals due to cost. Run the following on a Linux syslog server to see how much you’re filtering out:
`grep -c “filtered” /var/log/syslog` (Check for discards).
Step 2: On Windows, use PowerShell to check the Event Log sizes and ensure you are ready to store the “verbose” channels (like PowerShell Operational logs) which are often turned off.
`Get-WinEvent -ListLog | Where-Object {$_.RecordCount -gt 0}`
Step 3: Configure your SIEM ingestion tier to accept raw data into a “cold” storage (like S3 or Azure Blob) before filtering. Use AWS CLI to create a lifecycle rule that moves data to cheaper storage so you can afford to keep more.
`aws s3api put-bucket-lifecycle-configuration –bucket my-security-logs –lifecycle-configuration file://lifecycle.json`
2. AI-Assisted Alert Triage: Processing More, Not Fewer
The argument that AI will allow teams to do “more with less” usually refers to alert fatigue. However, if AI processes 100,000 alerts down to 10 critical incidents, the team will simply be asked to look at 1,000,000 alerts to find 100 incidents. To survive this, you must automate the response to the false positives, not just the triage.
Step‑by‑step guide explaining what this does and how to use it.
Implement a “pre-processing” agentic AI workflow that enriches alerts before they hit the analyst queue.
Step 1: Build a Python script that uses a local LLM (via Ollama) to score an alert based on asset criticality.
import requests
def enrich_alert(alert_json):
Query your CMDB API to check if the asset is production
asset_id = alert_json['host']
response = requests.get(f"http://cmdb.internal/api/asset/{asset_id}")
if response.json()['tier'] == 'critical':
alert_json['score'] = 100
return alert_json
Step 2: On Linux, use `jq` to parse JSON logs and pipe them into your AI enrichment engine.
`cat alerts.log | jq -c ‘.[]’ | python3 enrich.py`
Step 3: Automate the closure of “Informational” alerts via API. Use `curl` to update the ticket status in your SOAR (e.g., TheHive) if the AI score is below a threshold.
`curl -X PATCH -H “Content-Type: application/json” -d ‘{“status”: “Closed”}’ http://thehive:9000/api/case/{id}`
3. Vulnerability Management: Expanding the Attack Surface
If AI makes vulnerability analysis easier, it does not mean you scan less. It means you scan the “unscannable.” This includes IoT devices, legacy mainframes, and source code repositories. AI can now write the exploit code to verify a vulnerability, which ironically increases the urgency to patch.
Step‑by‑step guide explaining what this does and how to use it.
Leverage AI to automate the triage of CVEs.
Step 1: Use the National Vulnerability Database (NVD) API with a script to pull only CVEs with a CVSS score > 7 that have known exploits (KEV catalog).
`curl -X GET “https://services.nvd.nist.gov/rest/json/cves/2.0?cvssV3Severity=CRITICAL” | jq ‘.vulnerabilities[].cve.id’Step 2: On Windows Server, use PowerCLI (VMware) to snapshot VMs before attempting an AI-recommended "hot patch" that requires no reboot.Get-VM -1ame “Server01” | New-Snapshot -1ame “Pre-AI-Patch”`
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Step 3: Run an automated security scan using Nuclei (a fast vulnerability scanner). The more efficient it gets, the more often you will run it.
`nuclei -u https://internal-app.com -t ~/nuclei-templates/ -severity high,critical`
4. Cloud Infrastructure: The Complexity Multiplier
Cloud provisioning became easier, so teams built more environments. With AI writing Terraform scripts, developers will spin up infrastructure ten times faster. The security team must implement “Policy as Code” immediately. The Jevons Paradox here means that every new microservice created by AI must be hardened.
Step‑by‑step guide explaining what this does and how to use it.
Implement automated remediation using Azure Policy or AWS Config.
Step 1: Write a Rego policy (for OPA) to deny S3 buckets with public access. This ensures that while we have more buckets, they are secure.
package aws
deny[bash] {
input.public = true
msg = sprintf("Bucket %v is public", [input.name])
}
Step 2: Configure a GitLab CI pipeline to run `checkov` on every Terraform commit. This prevents AI-generated code from introducing misconfigurations.
`checkov -d ./terraform/`
Step 3: Use the AWS CLI to enforce encryption on all new (and existing) EBS volumes.
`aws ec2 enable-ebs-encryption-by-default`
5. API Security and Secrets Sprawl
As we do more work, we build more connections. AI assistants often help developers code faster, leading to them accidentally hard-coding API keys into repositories. The “more” that AI brings includes a massive spike in Secrets Sprawl.
Step‑by‑step guide explaining what this does and how to use it.
Implement GitLeaks and TruffleHog in your CI/CD pipeline.
Step 1: Install `trufflehog` on a Linux jumpbox and run it against your entire GitHub organization to detect the influx of new secrets.
`trufflehog github –org=my-company –token=GITHUB_TOKEN`
Step 2: For Windows, integrate with Azure DevOps. Use PowerShell to run a detection script during the build phase.
`trufflehog filesystem . –json | Out-File -FilePath secrets.json`
Step 3: Automate the revocation of keys. If a secret is found, trigger an AWS Lambda function to immediately deactivate the IAM key.
`aws iam deactivate-user –user-1ame `
What Undercode Say:
- Key Takeaway 1: The Jevons Paradox is not a bug in the system; it is a feature of capitalism and operational maturity. Expecting AI to reduce headcount is a fallacy; instead, it will force a re-skilling of the workforce to handle “higher-order” thinking.
- Key Takeaway 2: The immediate response to AI efficiency must be the hardening of infrastructure via “Policy as Code” and automated remediation, otherwise, the increased volume will lead to a catastrophic failure in mean-time-to-respond.
-
Analysis: Ross Haleliuk is correct to identify that efficiency drives consumption. In security, the “work” is defined by the adversary’s capabilities and the business’s risk appetite. As we automate the boring stuff, security teams will pivot to threat hunting, advanced forensics, and architectural reviews. This shift actually requires more senior people, not fewer. The role of the L1 analyst will evolve into an L2 “AI Handler” who manages the automation fleet rather than clicking buttons on a console. This creates a dynamic where the volume of alerts handled per person increases, but the total number of people doesn’t necessarily decrease because the attack surface has expanded to include AI-specific vulnerabilities (like prompt injection) which we didn’t have to deal with before. The industry must prepare for a “hyper-automation” phase where the speed of attack generation matches the speed of defense deployment.
Prediction:
+1 AI will force a structural shift in SOC design, moving from “triage centers” to “engineering hubs” where analysts write code and configure automation pipelines.
+1 We will see the emergence of “AI Firewalls” and “LLM Guardrails” as a mandatory layer in every enterprise stack, creating an entirely new sub-industry of cybersecurity.
-1 The “more with less” narrative will lead to vendor pushback and marketing conflicts, resulting in unrealistic expectations from the C-Suite and board of directors.
-1 If automation is implemented poorly, the increased alert volume will lead to “alert exhaustion” faster than traditional alert fatigue, increasing burnout rates significantly in the short term.
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