Our vision to mitigating ransomware attacks before criminals steal and encrypt data
Our vision to mitigating ransomware attacks before criminals steal and encrypt data
Our vision to mitigating ransomware attacks before criminals steal and encrypt data
A look at our strategic approach that leverages our knowledge of big data and advanced AI to detect attacker activity on a network after they've successfully breached classic detection systems
A look at our strategic approach that leverages our knowledge of big data and advanced AI to detect attacker activity on a network after they've successfully breached classic detection systems
A look at our strategic approach that leverages our knowledge of big data and advanced AI to detect attacker activity on a network after they've successfully breached classic detection systems
More than 2/3 of companies will undergo a ransomware attack this year, with breaches taking ~277 days to contain*
In a twist of fate, the network paths designed to keep our data flowing are the very ones cyber criminals use to conduct ransomware attacks.
Traditional ransomware defenses focus on preventing initial breaches or restoring infrastructure after systems are locked, but they do little to alert organizations of network activity after an attacker penetrates and persists within networks.
There exists a critical point during a ransomware attack, after the initial breach of a network and before data is stolen or systems encrypted, where attacker network communication can be detected and cut off.
Initial malware variants, typically distributed via successful phishing campaigns, are known as 'loader' or 'dropper' trojans. These trojans enable attackers to maintain persistence for further intrusion activities and to deploy subsequent-stage malware, including ransomware.
This loader malware establishes a connection to external attackers by exploiting standard communication protocols in unconventional ways. This allows the traffic to blend in as normal, enabling it to bypass traditional detection systems
Detecting this activity requires identifying subtle signals which emerge only through analysis of all network traffic across multiple spatial and temporal features such as time
More than 2/3 of companies will undergo a ransomware attack this year, with breaches taking ~277 days to contain*
In a twist of fate, the network paths designed to keep our data flowing are the very ones cyber criminals use to conduct ransomware attacks.
Traditional ransomware defenses focus on preventing initial breaches or restoring infrastructure after systems are locked, but they do little to alert organizations of network activity after an attacker penetrates and persists within networks.
There exists a critical point during a ransomware attack, after the initial breach of a network and before data is stolen or systems encrypted, where attacker network communication can be detected and cut off.
Initial malware variants, typically distributed via successful phishing campaigns, are known as 'loader' or 'dropper' trojans. These trojans enable attackers to maintain persistence for further intrusion activities and to deploy subsequent-stage malware, including ransomware.
This loader malware establishes a connection to external attackers by exploiting standard communication protocols in unconventional ways. This allows the traffic to blend in as normal, enabling it to bypass traditional detection systems
Detecting this activity requires identifying subtle signals which emerge only through analysis of all network traffic across multiple spatial and temporal features such as time
More than 2/3 of companies will undergo a ransomware attack this year, with breaches taking ~277 days to contain*
In a twist of fate, the network paths designed to keep our data flowing are the very ones cyber criminals use to conduct ransomware attacks.
Traditional ransomware defenses focus on preventing initial breaches or restoring infrastructure after systems are locked, but they do little to alert organizations of network activity after an attacker penetrates and persists within networks.
There exists a critical point during a ransomware attack, after the initial breach of a network and before data is stolen or systems encrypted, where attacker network communication can be detected and cut off.
Initial malware variants, typically distributed via successful phishing campaigns, are known as 'loader' or 'dropper' trojans. These trojans enable attackers to maintain persistence for further intrusion activities and to deploy subsequent-stage malware, including ransomware.
This loader malware establishes a connection to external attackers by exploiting standard communication protocols in unconventional ways. This allows the traffic to blend in as normal, enabling it to bypass traditional detection systems
Detecting this activity requires identifying subtle signals which emerge only through analysis of all network traffic across multiple spatial and temporal features such as time
We've discovered that when analyzing network behaviour across multiple dimensions, unique fingerprints emerge which can be used to distinguish between normal and malicious traffic.
Allow us to illustrate this example:
Consider the scenario where a host within an organization has become infected with a loader malware variant that successfully established a C2 (command and control) channel to the attackers.
Because most packets within a network are encrypted there is limited information that exists within a packet header to determine if it is malicious or benign
As a result classic detections systems have a hard time detecting this activity. Only when inspecting flows across additional dimensions does a pattern begin to emerge.
In this scenario, if time was used to measure the interval between sequential packets we can start to see a pattern difference between these two flows. One of the flows has more of a programmatic pattern.
The problem is that classic detections systems have limited visibility across a network and short time windows. Additionally the behavioral patterns of a network and a cyber attack cannot be algorithmically programmed, but instead require an advanced AI foundation model capable of learning and reasoning about traffic.
Prophet Detects Hidden Malicious Network Traffic
Our strategy which involves analyzing all traffic across multiple dimensions presents significant technical challenges.
Collect all traffic records in all environments
Achieving complete visibility is challenging for organizations today, as it involves aggregating and analyzing traffic across diverse environments, including on-premises and cloud infrastructures, as well as across virtual, physical, and containerized deployments. Prophet deploy's across all these environments and merges different traffic types, such as PCAP and NetFlow, providing a unified view of all network activity.
Collect all traffic records in all environments
Achieving complete visibility is challenging for organizations today, as it involves aggregating and analyzing traffic across diverse environments, including on-premises and cloud infrastructures, as well as across virtual, physical, and containerized deployments. Prophet deploy's across all these environments and merges different traffic types, such as PCAP and NetFlow, providing a unified view of all network activity.
Collect all traffic records in all environments
Achieving complete visibility is challenging for organizations today, as it involves aggregating and analyzing traffic across diverse environments, including on-premises and cloud infrastructures, as well as across virtual, physical, and containerized deployments. Prophet deploy's across all these environments and merges different traffic types, such as PCAP and NetFlow, providing a unified view of all network activity.
Hyper scale network traffic analysis
Modern networks produce enormous volumes of data, necessitating substantial computational resources and advanced traffic processing techniques for real-time analysis. Designed from the ground up to handle this challenge, Prophet efficiently processes network traffic and seamlessly scales to accommodate any volume.
Hyper scale network traffic analysis
Modern networks produce enormous volumes of data, necessitating substantial computational resources and advanced traffic processing techniques for real-time analysis. Designed from the ground up to handle this challenge, Prophet efficiently processes network traffic and seamlessly scales to accommodate any volume.
Hyper scale network traffic analysis
Modern networks produce enormous volumes of data, necessitating substantial computational resources and advanced traffic processing techniques for real-time analysis. Designed from the ground up to handle this challenge, Prophet efficiently processes network traffic and seamlessly scales to accommodate any volume.
Transformer-based AI analyzes all your traffic in real-time
Transformer neural network models excel at understanding the context within sequences, whether they're words in a sentence or packets in network traffic. This capability allows Prophetic AI to interpret the flow of network traffic not just as isolated events but as part of a larger narrative. This is crucial for identifying covert communication, which often relies on subtle, context-dependent patterns.
Transformer-based AI analyzes all your traffic in real-time
Transformer neural network models excel at understanding the context within sequences, whether they're words in a sentence or packets in network traffic. This capability allows Prophetic AI to interpret the flow of network traffic not just as isolated events but as part of a larger narrative. This is crucial for identifying covert communication, which often relies on subtle, context-dependent patterns.
Transformer-based AI analyzes all your traffic in real-time
Transformer neural network models excel at understanding the context within sequences, whether they're words in a sentence or packets in network traffic. This capability allows Prophetic AI to interpret the flow of network traffic not just as isolated events but as part of a larger narrative. This is crucial for identifying covert communication, which often relies on subtle, context-dependent patterns.
Together we can enforces the original promise of the internet—liberty, not liability.
Together we can enforces the original promise of the internet—liberty, not liability.
Together we can enforces the original promise of the internet—liberty, not liability.
PROPHETIC AI
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