Sqrrl
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Sqrrl provides a Linked Data Analysis platform to help organizations detect cyber security incidents by identifying important assets, actors, and events and revealing connections between them. This provides necessary context to monitor abnormal activity and assess damage from breaches. Sqrrl collects all relevant data into a unified system, allowing security teams to search and investigate much faster, in minutes rather than weeks. A Fortune 50 company used Sqrrl to improve the efficiency of its security operations center after an internal setback.
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1. The document provides an overview of best practices for implementing enterprise-wide data encryption and protection. It discusses challenges like explosive data growth, evolving compliance requirements, operational complexity, and increasing threats.
2. The document recommends a data-centric security approach that applies protection to data itself regardless of location. This includes discovering and classifying sensitive data, encrypting data in motion and at rest, and centralized key and policy management.
3. Effective data security requires discovering where sensitive data resides, encrypting that data, managing encryption keys centrally, and implementing access policies to control data use.
br-security-connected-top-5-trends
The document discusses five key security trends affecting security strategy: 1) Targeted attacks have revealed risks beyond just data exposure, requiring protection against these sophisticated attacks. 2) Data center transformation to software-defined services requires different security tailored to virtual/cloud constructs rather than traditional models. 3) Cloud security demands a strategy to keep data secure and compliant both in the cloud and to/from it. 4) Data protection must extend to intellectual property, risk management, and proof of due care. 5) Specialized environments like IoT shift security's role to protecting connected devices and their generated data.
Privacy preserving detection of sensitive data exposure
The document describes a privacy-preserving method for detecting inadvertent leaks of sensitive data in network traffic. The method allows an organization to outsource detection to a provider without revealing the actual sensitive data. It involves the organization computing digests of the sensitive data and sharing a sample with the provider. The provider searches traffic for matches and returns potential leaks. The organization then determines if any are true leaks, protecting the privacy of the sensitive data from the provider. The method was implemented and evaluated on real datasets, showing accurate detection while preserving privacy.
Microsoft 365 eEnterprise E5 Overview
Platform + Intelligence + Partners
This new understanding has led us to build new solutions for our customers. It informs our entire approach across three critical elements:
Building a platform that looks holistically across all the critical end-points we talked about – building security into our platform as well as providing security tools and technologies to you
Acting on the Intelligence that comes from our security-related signals and insights – helps you and us to detect threats more quickly
Fostering a vibrant ecosystem of partners who help us raise the bar across the industry – we know we’re not your only security vendor, and we want to work with the industry and take a holistic approach to technology
Microsoft 365 provides holistic security that is aligned to these four pillars of security.
By helping enterprise businesses secure corporate data and manage risk in today’s mobile-first, cloud-first world Microsoft 365 E5 enables customers to digitally transform by unifying user productivity and enterprise security tools into a single suite that enables the modern workplace.
Identity & Access Mgmt
Protect users’ identities and control access to valuable resources based on user risk level
Information Protection
Ensure documents and emails are seen only by authorized people
Threat Protection
Protect against advanced threats and recover quickly when attacked
Security Management
Gain visibility and control over security tools
The future of cyber security
The document discusses how security operations centers are adopting machine learning and artificial intelligence technologies to automate cybersecurity tasks like detecting threats, analyzing vast amounts of data, and responding quickly to incidents. It provides examples of how Oracle's cloud-based cybersecurity applications incorporate machine learning algorithms to continuously learn normal behavior, detect anomalies, and automate responses. The document advocates for adopting an intelligent, adaptive security framework that relies on AI and machine learning rather than static rules to manage hybrid cloud environments.
Understanding data lineage: Enabling Security Investigations | The Enterprise...
Understanding Data Lineage: Data lineage refers to the tracking and visualization of the data flow as it moves through an organization's systems, applications, and processes.
Lookingglass whitepaper
The document discusses threat intelligence and how Lookingglass' ScoutPlatform helps organizations leverage threat intelligence from multiple sources. It collects data on internet infrastructure and indicators of compromise from over 40 sources to provide context and a comprehensive view of risks. This aggregated intelligence helps security operations transition to a more proactive posture by providing timely and actionable insights.
Whitepaper- User Behavior-Based Anomaly Detection for Cyber Network Security
Whitepaper- User Behavior-Based Anomaly Detection for Cyber Network SecurityHappiest Minds Technologies
Fusion of data from multiple sources is generating new information from existing data. Now users can access any information from inside or outside of the organization very easily. It helps to increase the user productivity and knowledge shared within the organization. But this leads to a new area of network security threat, “Inside Threat”. Now users can share critical information of organization to outside the organization if he/she has access to the information. The current network security tool cannot prevent the new threat. In this paper, we address this issue by “Building real time anomaly detection system based on users’ current behavior and previous behavior”.HYBRIDIZED MODEL FOR DATA SECURITY BASED ON SECURITY HASH ANALYSIS (SHA 512) ...
High-profile security breaches and attacks on many organization’s database have been on the increase and the consequences of this, are the adverse effect on the organizations in terms of financial loss and reputation. Many of the security breaches has been ascribed to the vulnerability of the organization’s networks, security policy and operations. Additionally, the emerging technology solutions like Internet-ofThings (IoT), Artificial Intelligence, and Cloud Computing, has extremely exposed many of the organizations to different forms of cyber-threats and attacks. Researchers and system designers have made attempts to proffer solution to some of these challenges. However, the efficacy of the techniques remains a great concern due to insufficient control mechanisms. For instance, many of the techniques are majorly based on a single mode encryption techniques which are not too robust to withstand the threats and attacks on organization’s database. To proffer solution to these challenges, the current research designed and integrated a hybridized data security model based on Secured Hash Analysis (SHA 512) and Salting Techniques to enhance the adeptness of the existing techniques. The Hash Analysis algorithm was used to map the data considered to a bit string of a fixed length and salt was added to the password strings essentially to hide its real hash value. The idea of adding salt to the end of the password is basically to complicate the password cracking process. The hybridized model was implemented in Windows environment using python 3.7 IDE platform and tested on a dedicated Local Area Network (LAN) that was exposed to threats from both internal and external sources. The results from the test show that the model performed well in terms of efficiency and robustness to attacks. The performance of the new model recorded a high level of improvement over the existing techniques with a recital of 97.6%.
Big security for big data
Big organizations are dealing with massive amounts of data from various sources that needs to be collected and analyzed in real-time to detect security threats. This requires normalizing the data, integrating it from different sources, and using analytics to identify patterns and correlations that could indicate attacks. Doing this analysis in real-time allows threats to be addressed quickly before data is stolen, rather than only analyzing after an attack occurred.
10.1.1.436.3364.pdf
The user requirements of a new system for Railway reservation system may include:
1.Easy-to-use Interface: The new system should have a simple and intuitive user interface that allows users to quickly and easily access the web application and service providers to efficiently respond to requests.
2.Comprehensive Coverage: The new system should have an extensive coverage area that ensures drivers in all locations have access to timely and reliable assistance.
3.Integration with Modern Technologies: The new system should be fully integrated with modern communication channels and technologies, such as mobile devices and GPS, to allow for efficient and accurate communication between drivers and service providers.
4.Fast Response Times: The new system should ensure that service providers can quickly and efficiently respond to service requests, minimizing wait times for drivers in need of assistance.
5.Reliable Service: The new system should provide drivers with access to reliable and trustworthy service providers, ensuring that they receive high-quality service and repairs.
6.24/7 Availability: The new system should be available 24/7, ensuring that drivers can request assistance at any time of the day or night.
7.Transparent Pricing: The new system should provide transparent and fair pricing for all services, ensuring that drivers know what to expect and are not subject to unexpected or unreasonable charges.
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By meeting these user requirements, a new system for On Road Vehicle Breakdown Assistance can provide drivers with a reliable, efficient, and easy-to-use platform for accessing assistance and ensuring their safety on the road.
Detecting Unknown Insider Threat Scenarios
The document discusses detecting unknown insider threat scenarios. It proposes an ensemble-based, unsupervised technique to robustly detect potential insider threats, including scenarios not previously identified. The approach uses a variety of individual detectors combined using anomaly detection ensemble techniques. It explores factors like the number and variety of detectors, and incorporating existing knowledge from scenario-based detectors. The technique is evaluated on its ability to detect unknown scenarios in real data. Several new insider threat scenarios and solutions are presented, such as wearable technologies, outsourced systems, knowing detection methods, and activity outside work.
Sqrrl Datasheet: Cyber Hunting
One of Sqrrl's use cases is Threat Hunting, which involves proactively looking for cybersecurity threats before they trigger alerts.
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Cybersecurity and the Role of Converged Infrastructure June 2016
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Understanding data lineage: Enabling Security Investigations | The Enterprise...
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Sqrrl
- 1. Sqrrl : Linked Data Analysis: A Big Data approach to Cyber Security “Today, analysts are often found digging through log files, manually jumping from repository to repository to find and assemble the pieces to the puzzle,” remarks Mark Terenzoni, CEO, Sqrrl. A security breach leaves all sensitive and intellectual data of different formats at risk. Terenzoni believes that in several instances, existing cyber security solutions focus solely on alert-oriented data, which are difficult to prioritize because they only provide a limited view as to what’s going on. Sqrrl, a Big Data analytics platform provider, has a unique approach for detecting and investigating cyber security incidents by employing Linked Data Analysis. This allows administrators to quickly identify the important assets, relevant actors and events of an organization, accentuating the natural connections between them and providing contextual perspective. With this added context, it becomes much easier to monitor abnormal activity and assess the damage caused due to the breach. Sqrrl unifies multiple Big Data approaches into a single platform, which can be employed in use cases such as data breach, resulting from cyber-espionage, insider misuse and other types of complex attacks. Cambridge, MA based Sqrrl’s Big Data Analytics allows an organization to detect unusual activity by uncovering hidden connections in their data. Sqrrl Enterprise, their flagship product, developed using Linked Data Analysis policy, provides a way to visually investigate these connections. Sqrrl Enterprise enables its clients to ingest, secure, connect, and analyze massive amounts of structured and unstructured data. The product features streaming, encrypting, organizing bulk data with the entities and relationships, and fine-grained access controls. The solution also includes a web-based dash boarding service helps navigate through the data and relationships in the system. Additionally, Sqrrl ensures maximum security through techniques derived from ‘Knowledge Extraction’, and ‘Linked Data Analysis’. Data resulting from Linked Data Analysis is more useful with enriched contextual information, which includes additional details on the information itself as well as what it’s related to. Through its Enterprise approach, the company provides data-centric security to clients, enabling disparate data sources with various security and compliance requirements to be consolidated into a shared platform for processing, analytics, and application development. “We designed and delivered security on day one—not bolted it on as an afterthought,” admits Terenzoni, CEO. A Fortune 50 customer, based in the U.S, turned to Sqrrl to resolve an internal setback in their Security Operations Centre (SOC). Sqrrl, which is built with Big Data
- 2. technology, collected all the information needed for storage, processing and analysis in a unified system. This helped in reducing the time taken for the analyst team to conduct searches and investigations in support of cyber security review. The team can now retrieve secure records within minutes instead of weeks or days. The SOC now publishes self-serviced, on-demand investigations enabled by Sqrrl’s data-centric security approach, where every field of data in the system is individually protected. Sqrrl excels in use cases such as advanced data breaches resulting from cyber- espionage, insider misuse, and other types of hard-to-detect attacks “Our clients use Sqrrl Enterprise for a variety of real-time Big Data solutions, including cyber security analytics, healthcare analytics, and intelligence analysis,” revealed Terenzoni. Sqrrl Enterprises’ Big Data technology enables flexible and scalable storage of raw data for performing Linked Data Analysis. The company caters to the needs of analysts through dynamic knowledge extraction, aggregating and enriching the context available about the entities and relationships. “By giving users intuitive ways to explore their data and collaborate with their colleagues, Sqrrl helps to narrow the window between when events occur inside an organization’s network and being able to take action on them,” concludes Terenzoni, CEO.