When cybersecurity incidents strike, speed and precision are non-negotiable, and so is the human connection. For one global cybersecurity firm, every alert meant high stakes and high pressure, where Customer Success Consultants had to deliver expert guidance fast. Manual triage was slowing them down. By implementing Implicit's AI-powered pre-processing, the firm streamlined incident intake without sacrificing white-glove service, giving consultants everything they needed to act confidently, respond faster, and keep the personal touch their clients depended on.
The challenge
A global cybersecurity firm had a well-established protocol. When an incident such as a vulnerability, attack, or breach was detected, an automated email alert was triggered and sent to the assigned Customer Success Consultant. These alerts marked high-stakes moments where rapid, accurate, human-led responses were essential. The company intentionally avoided chatbot deflection for these interactions, viewing personal consultation as a core value-add.
While the human-first approach was non-negotiable, the process lacked structure and efficiency. Consultants often had to parse long documents, triage internal sources, and piece together incident context manually, all while under pressure. The organization needed a way to enhance the speed, consistency, and accuracy of how incident data was prepared and handed off, without replacing the human touch.
The solution: AI-powered incident pre-processing
To improve speed and quality while preserving white-glove service, the company implemented Implicit's AI platform to pre-process incident data, transforming raw alerts into curated, context-rich summaries delivered directly to Customer Success Consultants. From the moment an incident is detected, the data flows through a governed pipeline:
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1
Alert is triggered
Security systems detect a breach, threat, or anomaly and prepare an automated email alert for the consultant.
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2
Implicit pre-processing begins
Before the email is finalized, Implicit intercepts the alert pipeline and immediately begins enriching the report.
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3
Taxonomy classifies the incident
A preloaded, domain-specific taxonomy identifies what product is involved and what kind of issue occurred, enabling highly relevant retrieval.
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4
Knowledge graph is constructed
Implicit maps the incident to a curated graph connecting document sections, product models and configurations, known issues and resolutions, and past similar incidents.
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5
Graph and vector hybrid query
A graph query retrieves semantically related fixes, configs, and warnings. A vector query then pinpoints the most relevant passages, down to the specific step in a manual.
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6
Walled garden keeps it trusted
Retrieval is limited to a curated, high-confidence content set, so answers stay accurate with no hallucinations, efficient, and fully traceable by knowledge teams.
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7
Enriched summary reaches the consultant
The alert arrives with a root-cause hypothesis, a proposed resolution path, and references to the exact docs and past incidents, all in a single view.
3x more accurate than general RAG
Generic RAG pipelines retrieve loosely related text and leave consultants to verify it. Implicit's taxonomy, knowledge graph, and walled garden curation deliver recommendations that mirror what a senior consultant would conclude, three times more accurate than a general RAG baseline and 99% accurate in production.
Recommendation accuracy, relative to a general RAG baseline
The results
Implicit's pre-processing transformed how the firm manages real-time incident response, without compromising the human connection.
Accuracy in recommendations
Mirroring what senior consultants would derive, 3x more accurate than general RAG.
CSAT during high-severity events
Faster responses and deeper context let consultants focus on the client, not internal triage.
First contact resolution
Richer pre-processing resolved more incidents in the first interaction, cutting follow-ups.
Faster ramp for new consultants
AI-generated summaries leveled the playing field, shortening time to productivity for less experienced staff.
Human touch, powered by AI
The firm kept its white-glove model, now strengthened by behind-the-scenes AI delivering expert-level guidance.
Why simpler GenAI solutions were not enough
Many vendors offer generic RAG pipelines using LLMs and vector search. Implicit goes several steps deeper. Taxonomy preloading ensures domain-specific understanding of incidents. Graph queries enable intelligent retrieval from product-aware knowledge structures. Vector queries refine results based on context and relevance. Walled garden curation ensures every answer is trusted, relevant, and controlled. These layers are essential to delivering precision, speed, and credibility in high-stakes environments like cybersecurity.