How AI Coding Assistants Transform Security Teams into Tool Builders

Hold my coffee, I’m building a security tool” sounds like something reserved for engineers with computer science degrees. For a long time, that was true. Building security tools used to require deep technical specialization and came with real risk if something went wrong.

That’s not the world we’re in anymore.

I know this firsthand. My degree is in criminal justice, not computer science. I transitioned to cybersecurity because I loved investigations and problem-solving, but for years I assumed building software was beyond my reach. AI-assisted development changed that equation by closing the gap between identifying a threat and building a functional prototype to investigate it. I did not become a software engineer; I became someone who could test ideas and build lightweight tools that answered real investigative questions.

For security leaders, the shift to AI offers a massive talent multiplier for their workforce. AI-first security rewards domain curious and operational context far more than coding syntax. If an analyst understands the underlying threat, they already have the foundation to build a practical solution.

Why the cost of security prototyping has dropped to zero

This shift didn’t happen because cybersecurity suddenly got easier or because AI magically solves complex problems. It happened because the cost of learning and prototyping has dropped dramatically.

Not long ago, developing internal security tools meant upfront commitment: specialized technical talent, coordination across platform teams, and long‑term maintenance overhead. That structure made experimentation expensive and slow. AI coding assistants changed the ground rules.

Instead of writing complex code from scratch, analysts can describe an operational workflow in plain language and get a working prototype back in minutes. While it is not a finished production product, it is something real enough to test detection logic, parse non-standard telemetry, and eliminate repetitive triage tasks.

Leaders no longer need to fund multi-month software projects before knowing if a tool delivers business value. Teams can validate concepts cheaply on sample data and discard what fails without burning engineering cycles.

Modernizing security workflows without dedicated engineering queues

At the same time, security workflows are fundamentally changing. AI is increasingly handling scale, pattern recognition, and initial data synthesis. Humans are shifting toward roles as reviewers, guides, and decision‑makers who provide intent and domain judgment rather than manually executing every step.

In this environment, continuous experimentation is important. Commercial platforms excel at identifying and blocking standardized threats, but they rarely solve the long-tail operational friction unique to individual enterprises, like correlating custom application logs with legacy internal systems.

This is not an argument for cutting headcount. It is about fixing analyst burnout. When senior responders spend half their shifts copying data between disconnected dashboards, cognitive fatigue sets in and critical alerts slip through the cracks. Empowering analysts to prototype their own automation eliminates manual drudgery, keeps skilled investigators focused on high-severity threats, and shortens mean time to respond (MTTR).

Consider a familiar frustration: reviewing high-volume phishing reports or accessing anomalies and repeatedly asking the same questions. What is the blast radius? Have we seen this pattern across other endpoints? What step comes next in the playbook?

Instead of handling each case by hand or waiting on an overloaded platform team, an analyst can describe their triage process in plain language and ask an AI coding assistant to generate a targeted script or notebook. Testing it on sanitized sample data quickly reveals whether it accelerates investigations. If it helps, the team refines it. If it does not, they move on having learned something valuable.

Getting started does not require, a computer science degree, production access, A dedicated platform engineering queue, or a multi‑quarter development roadmap. It only requires a concrete operational problem you understand, domain curiosity, and a willingness to test small ideas in safe environments. If an analyst can explain an investigative bottleneck, they already have everything they need to start building practical solutions.

Establishing sanctioned sandboxes to neutralize shadow AI

When AI becomes easier to leverage as a tool, and security workflows become more AI‑centric, experimentation stops being optional. This isn't about chasing novelty. It's about accelerating operational tempo. The greater risk is standing still while modern threat actors continue to automate.

When organizations restrict AI coding tools without providing approved alternatives, frustrated analysts seeking to automate tedious triage often turn to unsanctioned public AI services, risking the exposure of sensitive incident logs and internal data.

Forward-looking security leaders can address this challenge by establishing sanctioned, low-risk experimentation sandboxes backed by enterprise-grade, zero-retention AI models. By pairing approved AI coding assistants with sanitized packet capture (PCAP) files, synthetic authentication dumps, and masked data, teams explore workflows safely.

Contained sandboxes establish clear boundaries:

• Prototyping environments stay strictly isolated from live production networks
• Workflows run against sanitized event samples with zero customer personal data
• Experimental tools execute in read-only mode without write access to production identity and access management (IAM) roles or network perimeters
• Small experiments answer the questions that cannot be resolved in architectural review meetings: Where does AI save measurable time? Where does it create confusion? And what decisions must remain strictly human-driven?

Reducing cognitive load with the PEAK Threat Hunting Assistant

Threat hunting is a great place to explore AI‑enabled security. Not because it’s easy, but because it’s fundamentally about asking good questions and exploring hypotheses.

To explore how agentic AI assists practitioners in real-world scenarios, the SURGe team developed the PEAK Threat Hunting Assistant. This open-source tool is designed to transform and accelerate the research and planning of hypothesis-driven threat hunts. Much like SURGe’s previous work exploring agentic AI, this project focuses on the practical implementation of agents to reduce cognitive load rather than replace human expertise.

Threat hunters constantly juggle large volumes of signals, fragmented context, and repetitive investigative steps. The goal with PEAK was not to automate threat hunting into a black box but helps analysts think more clearly and move faster.

PEAK uses AI to generate and refine targeted hunting questions, summarize fragmented event logs and threat telemetry across disparate sources, and suggest logical next investigative steps. The assistant makes zero autonomous decisions. Instead, it supports analysts' reasoning and keeps them in control by serving as a thinking partner.

The leadership lesson from PEAK is that impactful AI adoption does not require proprietary foundation models or massive platform overhauls.

A three-step framework for moving from idea to SOC impact

When frontline practitioners begin building their own tools, technology leaders face valid operational questions. Who maintains a script when an analyst leaves the company? How do teams prevent redundant tools from proliferating across the SOC? How do they protect against AI hallucinations or insecure package dependencies?

A mature AI prototyping strategy answers these questions with clear lifecycle governance:

1. Controlled experimentation. Analysts build standalone scripts or notebooks on local machines or cloud sandboxes. These tools operate on sanitized, non-production data with no long-term maintenance commitment.
2. Team validation. When a prototype proves consistently useful, it enters a shared internal repository and undergoes peer review. This centralized catalog prevents duplicate tool sprawl and provides a checkpoint for reviewing security, reliability, and dependencies.
3. Enterprise integration. If a micro-tool becomes a permanent component of daily operations, ownership transfers to DevSecOps or platform engineering. The tool is then integrated into enterprise systems with appropriate testing, documentation and change management.

This tiered approach allows frontline teams to innovate rapidly without leaving the enterprise dependent on brittle, unmaintained shadow tools.

Accelerating frontline analysts from idea to impact

Leaders succeed by normalizing experimentation: focusing on workflow bottlenecks rather than complex code architectures, keeping prototypes isolated in safe sandboxes, and keeping humans firmly in the loop.

Early experiments are supposed to be messy. AI will make confident mistakes, and feeling confused is a normal part of exploring new capabilities. As my high school basketball coach always said, “progress, not perfection.” The goal is not to eliminate errors entirely, but to keep them small, contained, and valuable for learning.

The security industry is pivoting toward AI-centric workflows whether we are ready or not. This shift directly favors organizations that empower their frontline domain experts. Real-world threat context, investigative intuition, and operational experience matter more than ever.

If you have been waiting until you or your team felt technical enough, do not wait. Start where you are, test small ideas in safe sandboxes, and learn by doing. In an AI-first world, building practical solutions has never been more accessible.

To explore more strategies for building an AI-first security culture and accelerating threat operations, subscribe to the Perspectives by Splunk monthly newsletter.