In today's digital landscape, cybersecurity professionals face an unprecedented challenge: sifting through mountains of data moving at lightning speed to identify potential threats. The task resembles finding a needle in a haystack—except this haystack is approaching at fiber optic velocity.
Organizations generate massive data volumes daily, establishing normal behavioral patterns. Meanwhile, cybercriminals create their own data footprints as they attempt to breach systems using sophisticated techniques. The critical mission? Identify these malicious needles before they inflict damage.
This data-intensive challenge makes it ideal for machine learning's computational power. Consequently, AI-powered cybersecurity solutions have flooded the market. However, these systems often introduce new problems, particularly overwhelming analysts with false positives that consume valuable time rather than conserving it.
MIT startup PatternEx operates on a fundamental principle: algorithms alone cannot adequately protect digital infrastructure. The company has pioneered a closed-loop approach where machine learning models identify potential threats, human experts provide feedback, and the system continuously learns from this input, enhancing its ability to flag only activities requiring human attention.
"Most machine learning systems in cybersecurity focus on anomaly detection," explains Kalyan Veeramachaneni, PatternEx co-founder and MIT principal research scientist. "This approach requires establishing a baseline of normal activity and typically operates unsupervised, generating excessive alerts that often lead to system abandonment. PatternEx's key innovation allows analysts to train the system, using their feedback to eliminate false positives effectively."
The result? Significantly enhanced analyst productivity. Compared to conventional anomaly detection software, PatternEx's Virtual Analyst Platform identified ten times more actual threats through the same daily alert volume. This advantage remained consistent even when competing systems generated five times more alerts per day.
Since its initial deployment in 2016, PatternEx's system now serves security analysts at major corporations across diverse industries, alongside firms providing cybersecurity as a service.
Combining Human Expertise with Machine Intelligence
Veeramachaneni joined MIT in 2009 as a postdoctoral researcher and now leads a research group in the Laboratory for Information and Decision Systems. While his work primarily focused on big data science and machine learning, he hadn't deeply considered applying these tools to cybersecurity until a 2013 brainstorming session with PatternEx co-founders Costas Bassias, Uday Veeramachaneni, and Vamsi Korrapati.
Ignacio Arnaldo, who collaborated with Veeramachaneni as an MIT postdoc between 2013 and 2015, joined the company shortly thereafter. Their experience building machine learning tools at MIT had taught them that successful solutions must seamlessly integrate artificial intelligence with human expertise.
"Many machine learning challenges arise because systems must work alongside human analysts," Veeramachaneni notes, emphasizing that detected threats still require human investigation. "Automation can't operate independently. Even in relatively simple applications like loan approvals, technology augments rather than replaces human decision-making."
The company's first partnership with a major online retailer provided real-world data to train models in identifying potentially malicious behaviors. Gradually, they taught their algorithms to recognize various attack types using data sources including Wi-Fi access logs, authentication records, and network user behavior patterns.
While early models excelled in retail environments, Veeramachaneni recognized the widespread struggle to implement machine learning across industries through conversations with executives at MIT—a subject PatternEx recently addressed in a published paper.
"MIT has excelled at connecting academia with industry during my decade here," Veeramachaneni remarks. He estimates conducting approximately 200 meetings with private sector representatives in the past six years through MIT's Industrial Liaison Program, discussing their challenges and ensuring his research addresses practical problems.
Beyond direct enterprise customers, PatternEx began offering its platform to security service providers and specialized teams that hunt for undetected cyber threats within networks.
Today, analysts can build machine learning models through PatternEx's platform without writing code, lowering barriers to AI adoption as part of what Veeramachaneni describes as the "democratization of AI" trend.
"Time efficiency is critical in cybersecurity—understanding attacks can't require hours or days," Veeramachaneni emphasizes. "Enabling analysts to build and customize machine learning models represents the most vital aspect of our system."
Empowering Security Analysts with AI Assistance
PatternEx's Virtual Analyst Platform transforms security analysts' workflow, making them feel supported by an army of assistants continuously analyzing data logs and highlighting the most suspicious network activities.
The platform employs machine learning models to process more than 50 data streams, identifying anomalous behaviors. It presents this information to analysts with supporting visualizations to facilitate decision-making. Once analysts determine whether flagged behavior constitutes an attack, this feedback updates the models across PatternEx's entire customer base.
"Before machine learning, organizations would typically detect attacks belatedly, name them, and share information reactively," Veeramachaneni explains. "Our approach leverages multiple customer datasets to identify threats in real-time and immediately check if other customers face similar risks—a process we perform efficiently daily."
Upon implementation with new customers, the system can immediately identify 40 distinct cyberattack types using 170 prepackaged machine learning models. Arnaldo notes that while PatternEx works to expand these capabilities, customers also contribute to the model base by developing solutions addressing their specific threat concerns.
Even customers without machine learning expertise can deploy PatternEx's system immediately, watching it become more intelligent automatically without specialized intervention.
By providing this flexibility, PatternEx delivers cutting-edge artificial intelligence tools to industry experts who understand their sectors most intimately. This approach reflects the company's founding philosophy: enhancing human capabilities with artificial intelligence rather than replacing them.
"Our system targets everyday security professionals rather than specialized data scientists—profiles that cybersecurity teams struggle to hire," Arnaldo explains. "We empower domain experts already on staff who possess the deepest understanding of their data and use cases."
