Doppel's AI agent cuts security operations workload by 30% in 30 days using OpenAI o1
Cybersecurity teams are overwhelmed by alert volumes; nuanced decisions about phishing takedowns require detailed manual analysis that is difficult to scale when ingesting over 10 million websites, social media accounts, and mobile apps daily.
Traditional machine learning filtered out obvious false positives but could not make the nuanced judgment calls required for takedown decisions, which require interpreting unstructured data such as screenshots, time-series activity, and customer-specific policies.
Doppel's AI agent automated 30% of security operations workload in under 30 days, exceeded human-level benchmarks with a lower false-positive rate and more genuine threats uncovered, and delivered faster response times to customers.
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Frequently asked questions
What did this team achieve with this AI workflow?
Doppel's AI agent automated 30% of security operations workload in under 30 days, exceeded human-level benchmarks with a lower false-positive rate and more genuine threats uncovered, and delivered faster response time…
What results were reported?
SOC workload reduction: 30%; Time to achieve 30% workload reduction: 30 days; False-positive rate vs human analysts: lower false-positive rate; Genuine threats uncovered vs human analysts: uncovered more genuine threats (source-reported, not independently verified).
What failed first in this deployment?
Traditional machine learning filtered out obvious false positives but could not make the nuanced judgment calls required for takedown decisions, which require interpreting unstructured data such as screenshots, time-s…
How is this incident management AI workflow structured?
Daily data ingestion → ML false-positive filter → AI agent nuanced classification → Continuous learning loop → Threat response delivered.