Introduction

During our hackathon, one submission stood out for tackling a problem that keeps security teams up at night: alert overload. Zeeshan Alam submitted an AI-Powered Incident Response Assistant (SOC Alert Assistant) — a focused, practical prototype that combines AI summarization, lightweight automation, and a conversational interface to make analysts faster and less exhausted. Built as a tight proof-of-concept, the assistant shows how automation can amplify human judgment in security operations without taking risks by acting blindly.

The Challenge

Security operations centers are strained by volume and context switching. Analysts are flooded with noisy alerts from endpoints, network sensors, and cloud services; much of that noise is low value, but it still needs triage. The day-to-day reality becomes a grind of opening logs, piecing together indicators, enriching data from multiple sources, and deciding whether an event needs escalation. That manual work wastes time, increases fatigue, and makes it harder to spot genuine threats quickly.

Zeeshan’s brief was clear: reduce time-to-acknowledge, improve the clarity of incident context, and surface safe, suggested responses so analysts only act when necessary. The goal wasn’t to replace analysts but to give them a fast, reliable assistant that turns raw telemetry into readable summaries and vetted next steps — preserving human control while removing busywork.

The Implementation

What makes Zeeshan’s approach strong is its practical focus on integration and human-in-the-loop safety. The SOC Alert Assistant ingests normalized event streams, runs AI-driven triage and summarization, and exposes suggested actions through a chat-like interface where analysts can review, query, and trigger remediations. The hackathon submission included synthetic Windows and Sysmon logs for demonstration and a clear report detailing the workflow.

At a high level, the pipeline works like this:

• Ingest and normalize. Logs and alerts from endpoints and sensors are collected and normalized into a consistent schema. For the hackathon demo, Zeeshan used sample Windows and Sysmon logs to model realistic alert traffic and fields such as timestamps, hosts, processes, and network indicators.
• AI triage and summarization. Each alert is passed through a natural language model that scores priority, extracts relevant observables, and produces a concise incident summary. Instead of raw, noisy chatter, analysts get a short, standardized note that captures the “what, why, and where” of the event.
• Suggested automated responses. For routine, low-risk tasks — quarantine host, block a suspicious IP, fetch related logs — the assistant proposes vetted remediation steps. Importantly, these are suggestions, not automatic actions. Each recommended step includes a rationale and an audit trail so the human reviewer can see why it was proposed.
• Chat-driven investigation. Analysts interact with the assistant via a conversational UI: ask for more context, request a deeper log pull, or accept a suggested remediation. The interface keeps the interaction lightweight and focused, enabling faster decision cycles.
• Logging and oversight. Every suggestion and action is logged for auditability and continuous improvement. Over time, suggested actions can be formalized into playbooks as patterns emerge.

This design intentionally balances speed and safety. The AI handles the repetitive parts — triage, enrichment, and summarization — while analysts retain final authority over remediation. That balance reduces risk and preserves trust in automation, which is essential for security teams.

The Achievements

Even as a prototype, the SOC Alert Assistant showed several practical wins that make it worth piloting in a real environment.

• Clear, actionable summaries. Translating raw alerts into short, consistent summaries lets analysts understand incidents in seconds rather than minutes. That compressed context speeds up triage and helps prioritize attention on what matters most.
• Reduced analyst fatigue. By automating enrichment and routine triage tasks, the assistant removes much of the repetitive work that causes burnout. Analysts spend less time hunting for context and more time applying judgment where it’s needed — a meaningful quality-of-life improvement for teams under pressure.
• Reusable investigation patterns. The assistant’s suggested actions effectively capture repeatable playbooks for common incidents. Those playbooks can be iterated into templates that new analysts can use, improving consistency across shifts and speeding up onboarding.
• Safe automation posture. The prototype follows a conservative automation philosophy: assist, don’t replace. Suggested remediations come with rationale and audit logs, enabling teams to tune thresholds, approve actions, and gradually trust automation as it proves itself.
• Demo-ready artifacts. Zeeshan provided a clear submission report and synthetic logs for validation, which makes the concept easy to reproduce and test. That kind of documentation is exactly what you want when moving a prototype into a pilot.

Taken together, these achievements show how a modest, well-designed assistant can multiply analyst throughput without increasing risk. The prototype demonstrates a realistic path forward: apply AI where it removes tedium, keep humans in the loop for nuance, and iterate with observable audit data.

The project earned the Runner-Up position in the Turbotic Automation AI Hackathon 2025.

“My goal was to reduce analyst fatigue by combining AI with human judgment — making SOC operations faster and smarter without losing control.” — Zeeshan Alam

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