DEV Community: Emani Sai Shanmukha Srinivas

Supercharging ThreatOps: Why Google Cloud's Agentic AI Defense Changes the SOC Game

Emani Sai Shanmukha Srinivas — Wed, 29 Apr 2026 03:24:58 +0000

Tuning into the Google Cloud NEXT '26 keynotes this week felt like watching the future of security operations unfold in real-time. From Thomas Kurian's opening keynote on April 22 to the deep-dives in the Developer Keynote on April 23, the focus was unmistakable: the era of AI experimentation is over, and the Agentic Enterprise is officially here. While the new Cloud Storage Rapid (hitting 15 TB/s) and the staggering scale of the new TPU 8t processors were impressive, the announcements that completely captured my attention were the launch of the Gemini Enterprise Agent Platform (the heavy-hitting evolution of Vertex AI) and the new Agentic AI Defense capabilities. Here is my take on what these updates mean for developers building next-generation security tools, and how they perfectly align with the architecture of modern real-time SOC dashboards. The Shift to Autonomous Security Agents The most significant takeaway for security developers is Google's push towards autonomous agents for Threat Hunting and Detection Engineering. The introduction of tools like the Dark Web Intelligence agent, powered by the latest Gemini 3.1 Pro models, represents a massive leap in how we parse external events. For developers, the new Agent Studio and Agent Development Kit (ADK) mean we are no longer just sending zero-shot prompts to an API; we are defining graph-based frameworks where specialized sub-agents collaborate to solve complex, multi-step problems in isolated, secure sandboxes. Evolving the ThreatOps Architecture. When architecting ThreatOps, the primary goal was to create a real-time SOC dashboard that could autonomously handle threat classification and map incidents directly to the MITRE ATT&CK framework. The existing ingestion pipeline relies heavily on Databricks for stream processing, with the Gemini API generating the ML scoring baselines. However, orchestrating complex reasoning across high-velocity threat data can be a massive bottleneck. The announcements from Next '26 provide the exact native building blocks needed to evolve this kind of architecture : Sub-Second Inference with TPU 8i: Real-time threat classification requires absolute minimal latency. The new 8th-generation TPU 8i chips—optimized specifically for inference and reinforcement learning with their Boardfly topology—mean that ML scoring baselines can evaluate potential breaches and execute logic virtually instantaneously. From APIs to Agentic Workflows: By migrating the current Gemini API integration over to the newly unified Gemini Enterprise Agent Platform, standard threat classification logic can be upgraded into a dedicated Detection Engineering agent. This allows the system to actively query enterprise databases like AlloyDB for historical context, maintaining a long-term understanding of previous attack patterns via the new Agent Memory Bank feature. Proactive Counter-Intelligence: Integrating the new Agent Security dashboard (powered by Security Command Center) means the platform can automatically map relationships between active agents and underlying models to scan for vulnerabilities, creating a closed-loop, proactive defense system. The Verdict: Beyond Surface-Level Automation. We are officially moving past the "reading the docs" phase of generative AI. The real power unveiled at Google Cloud NEXT '26 isn't just that the models are smarter; it's that the infrastructure is finally built to support multi-step, autonomous reasoning securely and at scale. For any developer building security pipelines, threat matrices, or enterprise dashboards, the Gemini Enterprise Agent Platform isn't just a rebrand of Vertex AI—it is a completely new paradigm for defensive engineering. I can't wait to refactor the pipeline and let these new agents loose on the data streams.

EcoSentinel: An Agentic AI Auditor for Earth Day 🌍

Emani Sai Shanmukha Srinivas — Sun, 19 Apr 2026 19:04:47 +0000

What I Built
EcoSentinel is an autonomous, multimodal environmental monitoring agent. It is designed to act as a "Security Operations Center (SOC) for the Planet," using vision-language models to analyze ecological data—ranging from satellite imagery to local photos of industrial waste—and generate structured remediation plans.

The core objective was to move beyond static data visualization and create an agent that can "see" environmental degradation, reason about regulatory compliance, and remember the history of a specific location to track its recovery over time.

Demo link - https://ai.studio/apps/45fd3395-6b00-4e5a-bb79-ea18db599750

To test it you can try this

Sector: Gran Chaco, Paraguay
Coordinates: -21.432658, -59.578124

Use the image below for the visual analysis

Demo of the same

Code
How I Built It
The project is built on an Agentic Workflow that prioritizes perception, memory, and verifiable identity:

Multimodal Perception (Google Gemini 1.5 Flash): I used Gemini 1.5 Flash via Google AI Studio to process high-resolution images. The model identifies environmental "threats" (e.g., deforestation, oil spills, or illegal dumping) and maps them against ecological standards.

Long-Term Memory (Backboard): To ensure the agent isn't just reacting to a single moment in time, I integrated Backboard. This allows EcoSentinel to store past audit results, enabling "Temporal Awareness." It can compare a new image of a site to one from six months ago to verify if restoration efforts are actually working.

Agent Identity (Auth0 for Agents): To prevent spoofed reports and ensure accountability, I implemented Auth0 for Agents. Every audit report generated by EcoSentinel is cryptographically signed with a unique machine-identity, making the data trustworthy for regulatory bodies.

Structured Reasoning: The agent doesn't just return text; it outputs structured JSON. This allows the data to be instantly piped into a dashboard or used to trigger automated alerts for environmental agencies.

Prize Categories
Best Use of Google Gemini: Leveraged Gemini 1.5 Flash for its massive context window and multimodal capabilities to analyze complex environmental imagery and output structured audit data.

Best Use of Backboard: Integrated Backboard to give the agent persistent memory across sessions, allowing it to track ecological site history.

Best Use of Auth0 for Agents: Used Auth0 to provide a verifiable identity for the AI agent, ensuring that every environmental report is authenticated and auditable.

Created for the Weekend Challenge: Earth Day Edition 2026.

I Built a Tool That Automatically Roasts Your Teammate Every Time They Use AI or Lose a Hackathon

Emani Sai Shanmukha Srinivas — Sat, 11 Apr 2026 13:49:18 +0000

This is a submission for the DEV April Fools Challenge

What I Built

dave-watch — a CLI tool that sits in the background, monitors your repo for AI-generated code commits and your team's hackathon history, and fires a personalised roast notification every single time Dave (or whoever your Dave is) does either of those things.

Did Dave just push a commit where every function has a docstring that starts with "Certainly!"? Roasted.
Did Dave's team just come last in a 48-hour hackathon where the only other team was two sleep-deprived undergrads? Absolutely roasted.

The notifications look like this:

Demo

$ dave-watch --target="dave@company.com" --hackathon-feed=devpost

👀 dave-watch is running. Watching Dave.
   (Dave does not know he is being watched.)
   (This will not take long.)

[09:14] ✅ No activity. Dave is in a meeting explaining
           why last sprint's tickets aren't done.

[09:47] 🚨 AI COMMIT DETECTED
        Commit: "refactor user service (cleanup)"
        Author: Dave <dave@company.com>
        Red flags found: 7
          • Docstring begins with "Certainly! Here's the..."
          • 3 nested try/catch blocks that all do the same thing
          • A comment that says "This is O(n log n) I think"
          • Variable: 'optimized_and_clean_final_v2_FINAL'
          • Dave pushed this 4 minutes after opening the file

        🎤 ROAST: "Dave asked an AI to write his code,
                   then asked a different AI to review it,
                   then asked Slack to explain the review.
                   Dave is a middle manager who learned to git push."

[11:30] 🚨 HACKATHON RESULT DETECTED
        Event:     HackIndia Regional 2025
        Teams:     18
        Dave's team placed: 17th
        Team above Dave: A solo 19-year-old who built
                         a Chrome extension that makes
                         fonts slightly more italic.
        Dave's post-mortem: "We were too ambitious"

        🎤 ROAST: "Dave's team had 4 people, a Figma file
                   with 38 screens, and a microservices
                   architecture for an app that showed
                   the weather. They did not show the weather.
                   They showed a loading spinner. For 3 minutes.
                   Then the tab crashed."

[14:02] ✅ No activity. Dave is on LinkedIn writing a post
           about 'lessons learned' from the hackathon.
           Dave has used the phrase 'fail forward' twice.
           Dave is not failing forward. Dave is failing sideways.

Code

# dave-watch.py
# Requirements: git, python3, devpost scraper, zero mercy

import subprocess
import time
import random

AI_TELLS = [
    "Certainly! Here",
    "As an AI language model",
    "I hope this helps!",
    "Here's the refactored",
    "optimized_and_clean_final",
    "# This function does",      # nobody writes this naturally
    "handle error here",
    "TODO: fix this later",
    "it works on my machine",
]

HACKATHON_ROASTS = [
    "Dave's team had a pitch deck with 22 slides. Slide 1 was the title. Slide 22 was 'Thank You'. The 20 slides in between were the vision. There was no product.",
    "Dave came to the hackathon with a pre-built template, renamed the variables, and called it a pivot. The judges called it something else.",
    "Dave's README had a 'Getting Started' section. Step 1 was 'Install Node.js'. There was no Step 2. The app did not start.",
    "Dave lost to a team who had never coded before. Their app crashed on launch. The judges said it had 'more soul'.",
    "Dave described his tech stack as 'scalable, cloud-native, and AI-powered'. It was a Google Form connected to a spreadsheet.",
]

AI_ROASTS = [
    "Dave has not written an original line of code since 2023. This is not an insult. This is a git log.",
    "Dave uses AI to write the code, AI to review the code, and AI to explain the code to himself. Dave is a project manager now. Dave does not know this.",
    "Dave copied the AI output, ran it, it failed, pasted the error back into the AI, copied the fix, and called this 'debugging'. Dave has been doing this for 4 hours. Dave will not read the error message himself.",
    "Dave's variable names are full sentences. This is because he copied the AI explanation and forgot to stop copying.",
    "The AI wrote a comment saying 'you may want to handle this edge case'. Dave deleted the comment. Dave did not handle the edge case.",
]

def check_latest_commit_for_ai(repo_path, target_email):
    result = subprocess.run(
        ["git", "log", "-1", "--author", target_email,
         "--pretty=format:%H", "-p"],
        cwd=repo_path, capture_output=True, text=True
    )
    output = result.stdout
    flags = [tell for tell in AI_TELLS if tell.lower() in output.lower()]
    return flags

def watch(target_email, repo_path, check_interval=300):
    print(f"👀 dave-watch is running. Watching {target_email}.")
    print(f"   (They do not know they are being watched.)")
    print(f"   (This will not take long.)\n")

    seen_commits = set()

    while True:
        flags = check_latest_commit_for_ai(repo_path, target_email)

        commit_hash = subprocess.run(
            ["git", "log", "-1", "--author", target_email,
             "--pretty=format:%H"],
            cwd=repo_path, capture_output=True, text=True
        ).stdout.strip()

        if flags and commit_hash not in seen_commits:
            seen_commits.add(commit_hash)
            print(f"🚨 AI COMMIT DETECTED")
            print(f"   Red flags: {', '.join(flags)}")
            print(f"\n   🎤 ROAST: {random.choice(AI_ROASTS)}\n")

        time.sleep(check_interval)

if __name__ == "__main__":
    watch(
        target_email="dave@company.com",
        repo_path=".",
        check_interval=300
    )

How I Built It

Tech stack:

Python 3 (for the git log parsing and general Dave surveillance)
subprocess (to run git commands with the energy of someone filing a formal complaint)
A curated list of AI tells scraped from three months of reviewing Dave's PRs
Devpost's public results pages (parsed manually, with increasing personal investment)
Anger

The process:

Dave pushed a PR. The first line of every function was a docstring that began with "Certainly!". I approved it anyway. I think about this every day.
Dave entered HackIndia Regional. Dave's team had a Notion doc with 6 pages of planning. Dave's team had zero pages of working code at submission time. Dave's team submitted the Notion doc.
I built this tool.
The tool works. Dave does not know it exists.
Dave has triggered it 11 times this week. It is Wednesday.

Known issues:

The tool has no off switch, which feels appropriate.
It will also flag your own commits if you've ever copied from an AI. You have. I have. We don't talk about this.
The hackathon scraper breaks if Dave doesn't enter under his real name. Dave once entered a hackathon as "DaVinci_Codes". The scraper found him anyway.

Prize Category

Community Favorite — because every team has a Dave. You read this and you thought of your Dave. You smiled a specific smile. This tool is for you.

Also: HTTP 418. I am a teapot. Dave is also a teapot. Dave does not know he is a teapot.