ClumL's Breakthrough Technology: Shaping the Future of AI
In-House Clustering Innovation
At ClumL, we've redefined clustering. Our proprietary engine, crafted in-house, efficiently manages both structured and unstructured data with ease.
Superiority of Clumit
Clumit, our clustering prodigy, outpaces and outperforms other open-source clustering engines. The cornerstone? ClumL's distinct algorithmic approaches that place us at the vanguard of clustering technology.
Commitment to Real-Time Excellence
Over half a decade of relentless effort has empowered us with expertise in vital clustering algorithms such as pattern recognition, hierarchy, DBSCAN, and OPTICS. The upshot? Clumit's unmatched speed, primed for the intense demands of real-time data flows.
Decoding Machine Learning
Translating unsupervised learning outputs is no mean feat. They often appear as abstract numbers, challenging to decipher. But for ClumL, challenges are opportunities.
Reimagining Labels with Regular Expressions
We've bridged the chasm between machine computations and human cognizance. By leveraging regular expressions, we craft descriptive patterns, seamlessly connecting machine outputs to our expansive existing knowledge database.
Patented Real-Time Regex Expertise
Our innovation journey is adorned with our patented real-time regular expression generation technology. A pioneering achievement, and a ClumL exclusive.
Big Data Platform
The artificial intelligence ecosystem is still young
The AI landscape is still unfolding. Many areas are not yet ripe to fully embrace its vast capabilities. ClumL's groundbreaking journey in unsupervised learning AI, facilitated by our distinct clustering engine, repeatedly underscored the limitations of the current ecosystem's support.
We need an ecosystem for unsupervised learning clustering
Distinct from supervised learning, which involves intermittent data collection and training, unsupervised learning possesses its unique demands. It requires continuous clustering of evolving data. This, especially in realms like security, necessitates real-time data feed and management of vast data volumes.
Creating a Big Data Platform for Massive Clustering
Every decision at ClumL, whether about communication protocols, data management strategies, API interfaces, or coding languages, was anchored in performance. Our past experiences reinforced the imperative of employing state-of-the-art methodologies; any deviation often led to glaring performance bottlenecks.
Stability is also important...
Striving for peak performance inevitably ushered in challenges around system stability. Marrying these seemingly contradictory goals—high-speed and steadfast stability—emerged as a cardinal challenge in our software development journey.
Adopting Rust as the main development language
Rust wasn't our initial choice for a development language at ClumL. But as we ventured deeper, modules demanding both swift performance and robust stability nudged us towards it. Over time, a significant portion of ClumL's software architecture was powered by Rust.
Toward an open platform
ClumL's commitment extends beyond proprietary systems. Our big data platform stands as a testament to openness. Our vision is set on its continuous evolution, showcasing its adaptability and relevance across diverse environments.
ClumL’s Innovative AI Security
Detect emerging threats and variants
Traditional security solutions are a flash in the pan
Whenever new security solutions emerge, they flaunt the advantages they possess over their predecessors. A key bragging right is their threat detection capability. Let's say an existing solution A identifies 10 threat types and a newer solution B identifies 20. Naturally, B seems superior. However, this advantage fades swiftly when B fails to detect beyond those 20 types.
Legacy security solutions fail to detect new threats
It becomes inconsequential whether a solution detects 10 or 20 threats. Given the dynamic nature of computing, many older threats become obsolete. Conversely, novel threats arise. Even if these aren't entirely new, they often represent variations of older threats, making detection tricky for conventional security tools.
Can we consistently detect new threats?
Users yearn for security solutions capable of identifying new or morphed threats. Regrettably, until the advent of AI, no solution truly met this expectation consistently.
Artificial intelligence is the only way to detect new and variant threats
This underscores our commitment at ClumL to intertwining artificial intelligence with security. One might wonder: Why did the traditional security solutions falter against new or evolved threats?
Threats detected by rule-based IPS
Traditional Security Solutions - Rule-Based
Before AI's integration with security, the vast majority of solutions were rule-driven. These solutions deemed any data conforming to a pre-defined rule set as a threat. If experts had prior knowledge of certain data being malicious, they set rules for it. Additionally, explicit markers within malicious data, whether inherent or discerned through observation, were crystallized into rules or signatures.
There are no rules for new threats
The snag is that these rules demand human crafting. Until a new threat is encountered, analyzed thoroughly, and a rule is crafted for it by cybersecurity experts, it remains undetected. In essence, such emerging threats elude recognition.
Instead of predetermined rules, we need artificial intelligence to detect them
Artificial intelligence, especially the unsupervised learning methodology, offers a ray of hope in this regard. Unsupervised learning, particularly clustering, is the prime AI technique for anomaly detection. One of the cardinal objectives of clustering is the discernment of outliers or anomalies.
Clumit Security - A threat detection solution that works
ClumL's artificial intelligence-backed security solution, dubbed Clumit, stands as the pinnacle for detecting nascent threats. Clumit delivers on a promise every security professional covets: detection of novel threats.
Align with security experts or users
Security solutions require human verification
Even the most advanced security solution demands the human touch to validate its conclusions, especially if it leverages artificial intelligence to identify novel threats.
Artificial intelligence and machine learning demand human oversight
Artificial intelligence, predominantly machine learning, learns from historical data, drawing from experience rather than logic. Given that past experiences might not always be accurate, the results from machine learning cannot be blindly trusted. Furthermore, unsupervised learning often demands more human intervention than its supervised counterpart.
Infuse human intellect into the outcomes of artificial intelligence
The potential threats flagged by unsupervised learning necessitate human verification to confirm them as genuine threats. This step can make some users apprehensive. A prevailing misconception is that AI, being a sophisticated tool, must invariably surpass human judgement. While AI might outperform human insights at times, it's not infallible. Experience-based AI isn't guaranteed to be 100% reliable.
Streamline human intervention to alleviate discomfort
It's impractical to envision a scenario devoid of human intervention. The primary aim should be to refine this process, making it more efficient and less intrusive for humans.
ClumL's artificial intelligence security solution, Clumit, integrates several user-friendly features. These tools assist users in effectively navigating and discerning emerging threat candidates.
At ClumL, our ambition is to achieve a perfect synthesis of artificial intelligence and human intellect.
