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Introduction:
The cybersecurity industry is currently captivated by the promise of agentic AI—systems that can reason, plan, and execute tasks autonomously. However, as highlighted by Principal Security Architect Adan Álvarez Vilchez, the rush to implement “agentic” solutions for every problem is creating a dangerous blind spot regarding cost, reliability, and scalability. While large language models (LLMs) like Opus 4.6 create “wow moments” by mimicking human reasoning, they often introduce unpredictable latency and variable results that are antithetical to the precision required in security operations. The core argument emerging from this discourse is a call for a hybrid approach: using agents to build automations, rather than allowing agents to become the automation themselves, particularly in high-stakes environments like code review and Security Operations Centers (SOCs).
Learning Objectives:
- Differentiate between deterministic automation and non-deterministic agentic AI to apply the appropriate solution in security workflows.
- Analyze the cost and performance implications of using LLMs for tasks traditionally handled by static analysis tools.
- Implement a hybrid security architecture that leverages AI for complex reasoning while maintaining deterministic controls for critical processes.
You Should Know:
1. The False Economy of Agentic Code Review
The recent hype surrounding AI-powered code review tools, such as ” Code,” serves as a cautionary tale. Initial market fears suggested these tools would disrupt the cybersecurity industry, causing stock fluctuations. However, a deeper look into the operational metrics reveals a different reality.
– The Cost Analysis: With costs estimated between $15 and $25 per review and an average completion time of 20 minutes, agentic code review is significantly slower and more expensive than traditional methods. Furthermore, these systems often fall into a recursive loop, reviewing code they generated themselves, which adds little security value.
– The Deterministic Alternative: Traditional Static Application Security Testing (SAST) tools operate on fixed rules and pattern matching. They run in seconds and operate on a predictable subscription model.
– Linux Command for Context: To understand the scale, consider scanning a codebase with a classic tool like `grep` for sensitive information:
Recursively search for potential AWS keys in a code repository
grep -r -E "AKIA[0-9A-Z]{16}" /path/to/codebase/
This command executes almost instantly, demonstrating the speed of deterministic pattern matching versus the latency of an LLM reasoning loop.
- The Token Drain in Security Operations Centers (SOCs)
In a SOC environment, the promise of agents sifting through logs to “reason” about threats is tempting, but it introduces a continuous financial liability. If every log event triggers a reasoning loop, the token consumption becomes astronomical.
– The Agentic Flow: An agent receives a log, decides it needs context, queries a knowledge base, formulates a hypothesis, and writes a report. This consumes thousands of tokens per alert.
– The Hybrid Flow: A deterministic rule first filters the noise. Only the remaining 5% of alerts are passed to an LLM for deep contextual analysis.
– Windows PowerShell Command for Log Filtering:
Deterministically extract all failed logon events (Event ID 4625) from the Security log Get-EventLog -LogName Security -InstanceId 4625 -After (Get-Date).AddHours(-24) | Export-Csv -Path "failed_logons.csv"
This deterministic filter reduces the dataset by 99%, ensuring an agent only spends tokens on the critical subset.
3. The “Black Box” Risk in Non-Deterministic Systems
Security relies on repeatability. If a vulnerability is detected, the process to find it must be reproducible. Agentic systems, by their nature, introduce variability. An agent might find a misconfiguration one day and miss it the next because its “reasoning path” changed.
– The Risk: You cannot guarantee the same output from the same input, which is a fundamental requirement for compliance audits (like SOC2 or ISO 27001).
– The Fix: Use agents to generate the deterministic scripts.
– Example: Ask an AI to “Write a Python script to check if S3 buckets are publicly accessible using Boto3.” You then run the script (deterministic) in production, not the agent. This ensures the check is identical every time it runs.
4. Cloud Hardening: The Deterministic Foundation
Before letting an agent loose on your cloud environment, the infrastructure must be hardened with deterministic Infrastructure as Code (IaC). You cannot have an agent dynamically fixing security groups if the baseline is not defined in code.
– Tool Configuration (Terraform): Define a rule that prevents public ingress.
Terraform snippet to enforce a security group rule
resource "aws_security_group_rule" "no_public_ssh" {
type = "ingress"
from_port = 22
to_port = 22
protocol = "tcp"
cidr_blocks = ["10.0.0.0/8"] Deterministically restricts to internal IPs
security_group_id = aws_security_group.web_sg.id
}
By defining this in Terraform, you remove the “decision” from an agent. It is a hard-coded rule applied every time.
- API Security: Deterministic Rate Limiting vs. Agentic Throttling
APIs are a primary attack vector. An agentic system might notice a DDoS attack and decide to throttle traffic. By the time it decides, the damage may be done.
– The Better Way: Implement deterministic rate limiting at the gateway level (e.g., Nginx or AWS WAF).
– Nginx Configuration:
Limit requests to 10 per second from a single IP address
limit_req_zone $binary_remote_addr zone=mylimit:10m rate=10r/s;
server {
location /api/ {
limit_req zone=mylimit burst=20 nodelay;
proxy_pass http://api_backend;
}
}
This rule is applied at the kernel level with zero “thinking time,” stopping the attack before the application layer even sees it.
6. Vulnerability Exploitation: The Speed of Automation
Penetration testers are beginning to use agentic AI to map out attack paths. However, for actual exploitation, deterministic tools are still superior for speed.
– Command Execution: An agent might suggest using a tool like sqlmap, but running `sqlmap` itself is a deterministic automation of SQL injection techniques.
Deterministic exploitation tool sqlmap -u "http://target.com/page.php?id=1" --dbs --batch
This tool runs through a pre-defined set of payloads and logic. It is fast and thorough. An agent trying to “reason” its way through an injection would be impractically slow.
What Undercode Say:
- Cost Inefficiency: Agentic AI, while powerful, is currently too expensive and latent for high-volume, repetitive security tasks like log filtering or SAST scanning.
- The Hybrid Mandate: The future of cybersecurity architecture is not purely agentic or deterministic; it is a managed hybrid where deterministic workflows handle the heavy lifting, and agents handle the edge cases requiring genuine reasoning.
- Tooling Over Thought: In a security context, you want a tool that executes a fix the same way every time (deterministic), not a “thinker” that might solve the problem differently depending on its mood. We must leverage AI to build more robust tools, not to replace them.
The analysis presented by Vilchez is a critical reality check. We are currently in a hype cycle where the capabilities of AI are overstated, and the operational costs are understated. The rush to replace established, deterministic security tools with agentic overlays ignores the fundamental economic principle of scale: fixed costs (automation) will always scale better than variable costs (tokens) for routine tasks. The cybersecurity professionals who succeed will be those who use AI as a force multiplier to write better YARA rules, more efficient Sigma detections, and more secure Terraform plans, rather than those who try to insert an LLM into the data path of every packet and log.
Prediction:
Within the next 18 months, we will see a market correction in “AI Security.” Startups that purely offer agentic “black box” solutions will struggle to retain enterprise customers due to unpredictable billing and compliance issues. Conversely, platforms that offer “Agentic-Assisted Automation”—where AI generates, tests, and validates deterministic security scripts for human deployment—will dominate the market. We will likely see a new category of “Automation Compilers” for security, where natural language requests are translated into provable, deterministic security controls, effectively using the “wow” of AI to build the “boring,” reliable infrastructure we actually need.
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Reported By: Adan %C3%A1lvarez – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
