MCAP Intelligence and Cyber Security

MCAP Security Intelligence Platform was designed for efficient mass and/or targeted interception of different communication sources and channels to support law enforcement agencies and various intelligence agencies.

• Covers the whole process from acquisition to presentation for law enforcement agencies’ (LEA) intelligence purposes and consolidate existing solutions, providing an overall picture of potential target activities’ thereby improving the success rate for mass lawful interception
• Improves cooperation and communication of all agencies involved on both national and international levels by providing a unique platform with a unified communication concept and common protocol
• Includes a number of passive and/or active acquisition probes, processes acquired traffic meta-data and content, and collects and classifies acquired material
• Can be critical in recognizing potential terrorist threats and activities by consolidating all inputs on a national level
• Was designed to protect data from unauthorized access including full audit trails, access control, and automatic consistency checks and alarms

MCAP for Financial Services

• Interday Liquidity – determine companies’ daily Global Cash Positions in real-time and by ingesting and analyzing all trade data in order to maintain adequate regulatory liquidity
• e-Discovery requirements – meet organizations’ e-Discovery requirements for risk and legal compliance and retain all of the needed business communications and report them in a timely manner
• Trading Analytics – In an industry where ‘milliseconds matter’, MCAP provides data in near, or real-time for executing trades done before the competition. The target of MCAP is fact based decisions and actions quicker and more correctly.
• Compliance/Regulatory Reporting, Fraud and Risk Mitigation – Regulatory requirements are a driving force behind forensic MCAP Big Data analytics. A key benefit of forensic MCAP Big Data analytics is the ability to detect potential trading transgressions that could have been detected prior.

MCAP for Healthcare

• Healthcare Population Health – Comparative-effectiveness researchers are combing a variety of databases for proof of the best, most cost-effective treatments for hundreds of conditions-information that could transform health care policy.
• Genomic Research – The routine procedures in Genomics can easily produce petabytes of data with the possibility of further data explosion after gene analysis. When genetic data and gene sequencing are added to the mix, providers have powerful tools to spot health risks within patient populations.
• Medical Fraud – An estimated $125 to $175 billion is lost annually thanks to fraud and the abuse of healthcare services. By analyzing all of the disparate data spread across the healthcare system, Big Data Analytics help organizations root out fraud before it happens. Some examples could be, intentional misrepresentation of services that result in higher payments; billing of unperformed services; or the deliberate delivery of unnecessary and inappropriate services for the express purpose of receiving the payment.
• Sensor-based (Telehealth) – Telehealth solutions have a profound impact on patient care, its quality and safety, and can also help drive costs out of the healthcare system. Some solutions that will need access to data from disparate sources include, allowing doctors to perform wellness check-ups or counseling online or over the phone, providing mobile medical centers for telemedicine services in rural areas, or making it easier for doctors to become licensed to treat patients in multiple states via videoconference or online communication.
• Real Time Reporting – A system that allows for the efficient management and sharing of all Big Data and providing real-time reporting to all departments. This could include revealing patient flow bottlenecks, tracking trending data per stage of care or department, comparing performance against hospital and healthcare benchmarks, assessing equipment utilization and fleet management, or tracking potential exposure to contagious disease or infection.

MCAP for Media and Entertainment

• A 360 degree view of the customer – Use MCAP to increase revenues, understand real-time customer sentiment, and increase marketing effectiveness. This allows companies to track user interactions across systems and channels in real time to personalize the user experience, improve website stickiness, increase customer loyalty, and increase ratings and viewership.
• e-Discovery requirements – Meet e-Discovery requirements for risk and legal compliance and retain all of the needed business communications and report them in a timely manner.
• Fraud and Risk Mitigation – Regulatory requirements are a driving force behind forensic MCAP Big Data analytics. A key benefit of forensic MCAP Big Data analytics is the ability to detect potential misconduct that could have been detected prior.
• Advertising and Campaign Management – MCAP enables digital advertisers to analyze the massive amount of IoT, Social Media, sensory, and personal data that consumers share, and offer those consumers more personalized and targeted ads for products and services they would use. MCAP Big Data can also provide companies with the ability to automated advertising solutions by pulling in all aspects of Big Data on a real-time basis.

MCAP for Telecommunications

• Telecommunication providers are seeing an unprecedented rise in volume, variety and velocity of information due to next generation mobile network rollouts, increased use of smart phones and rise of social media.
  • MCAP can deliver smarter services for new sources of revenue
  • MCAP provides operational efficiencies with a higher quality of network operations for better services
  • Build smarter networks with MCAP to drive consistent, high-quality customer experience for “Know Your Customer” campaigns, especially for customer churn prevention
• e-Discovery requirements – meet e-Discovery requirements for risk and legal compliance and retain all of the needed business communications and report them in a timely manner
• Fraud and Risk Mitigation – Regulatory requirements are a driving force behind forensic MCAP Big Data analytics. A key benefit of forensic MCAP Big Data analytics is the ability to detect potential misconduct that could have been detected prior.
• MCAP Security Intelligence Platform was designed for efficient mass and/or targeted interception of different communication sources and channels for telecommunications operators.
• Cyber Security – Network monitoring tools and a variety of other sensors across enterprise environments take in terabytes of new data every day, including IP addresses, network traffic, e-mails and log files to currently manage internal cyber-security (hacking from within). To manage internal and external cyber-security, however, an organization has to add external sources. Adding MCAP Big Data increases the load on current systems exponentially. This includes data from IoT, SMS, audio, video, sensor data, and more.

MCAP for Government

• Compliance/Regulatory Reporting, Benefits Fraud and Risk Mitigation – Regulatory requirements are a driving force behind forensic MCAP Big Data analytics. A key benefit of forensic MCAP Big Data analytics is the ability to reduce fraud and errors and boost the collection of tax revenues.
• e-Discovery Requirements – meet their e-Discovery requirements for risk and legal compliance and retain all of the needed business communications and report them in a timely manner.
• MCAP Security Intelligence Platform was designed for efficient mass and/or targeted interception of different communication sources and channels to support law enforcement agencies and various intelligence agencies.
• Cyber Security – Network monitoring tools and a variety of other sensors across enterprise environments take in terabytes of new data every day, including IP addresses, network traffic, e-mails and log files to currently manage internal cyber-security (hacking from within). To manage internal and external cyber-security, however, an organization has to add external sources. Adding MCAP Big Data increases the load on current systems exponentially. This includes data from IoT, SMS, audio, video, sensor data, and more.

MCAP for Insurance

Insurance companies can use data generated from sensors in automobiles to offer drivers rates based on the amount of driving they do, their driving habits, and even where they drive and park. In addition, companies can actually determine if drivers with safe driving policies are actually safe drivers, or just have not been involved in an accident or have been ticketed.

With all of this sensor data coming into an Insurance company, the analysts can now cross correlate the sensory data with policy management to ensure that their policies actually match the drivers’ patterns.

This cross correlation of data involves the ingest of disparate sources of data, including sensor data, enterprise data, email, CRRs from call centers, and more, into a singular repository for quick query and analysis results.

There are, however, some inherent issues that they face with today’s technology.

• The initial issue arises with the sensor devices that read driving pattern data in real time, for, as an example, 1 million sensors (policy holders). That would result in over 7 billion records per day or ~ 2 ½ trillion per year! Our current small PoC system stores ~12 trillion; which is equal to 5 years of data! Of course, we scale much higher and can store as many records as a customer has.
• Once the sensor data is stored, how can an organization cross-correlate with their enterprise (customer) data? All data in MCAP is accessible via SQL, so any application that speaks SQL can transparently expand their “data universe” with MCAP tables and then invisibly send queries to MCAP.
• The next issue is concerning the speed of queries and reports in order to provide near, or real-time analysis on this correlation of data in order to provide more optimized levels of policy management, thereby, enabling the policy holders to drive more ‘sanely’ and saving the Insurance company money by more optimized policy management.
• Currently, in our small PoC system, we have stored ~ 5 trillion records and are seeing query and response times on average of .02 seconds!

MCAP for Energy and Utilities

• Utility companies are realizing that metering data can be analyzed in ways that generate real insight into the utilization of consumer power
• Utilities and their commercial clients and customers must protect physical plants and associated assets against the potential impact of terrorist attacks against critical national energy and power infrastructures

There are, however, some inherent issues that they face with today’s technology.

 

1. The initial issue arises with the smart meters that read power consumption, or infrastructure viability data every 15 minutes for, as an example, 30 million sensors or smart meters. That would result in 3 billion records per day or ~ 1 trillion per year! Our current small test system stores ~12 trillion; which is equal to 12 years of data!
2. Once the sensor data is stored, how can an organization cross-correlate with their enterprise (customer) data? All data in MCAP is accessible via SQL, so any application that speaks SQL can transparently expand their “data universe” with MCAP tables and then invisibly send queries to MCAP.
3. The next issue is concerning the speed of queries and reports in order to provide near, or real-time analysis on this correlation of data in order to provide more optimized levels of power consumption to the consumers

MCAP for Sensor Data Analytics enables enterprises to collect and analyze machine data from virtually any source – regardless of volume, format, or location. This includes servers, virtualization infrastructure, network devices, energy and security infrastructures, custom and 3rd party applications, databases, RFID scanners, and more.

MCAP solutions (smallest models) can load over 500 billion sensor records per day into a singular repository, and together with all enterprise data, cross correlate all pertinent data to provide near real-time analysis.

 

MCAP for Oil and Gas

In the oil and gas industry, seismic exploration data is helping companies find profitable locations with less experimental drilling, lowering both operational cost and environmental impact. The current situation in the upstream side of oil and gas industry is that there are disparate point data sources which are still archived and processed in a multiple silo’ed environment. These various data sources can consist of financial data, geospatial data, drilling data temperature sensors, real-time SCADA data from drilling events, asset management, equipment safety management, employee management, weather conditions (IoT) and enterprise data.

These can be broadly segmented into 2 data classes:

 

1. Velocity or Small Data (Real time streaming data streams) and
2. Big Data (Unstructured data, geospatial data, safety records, surveillance video streams, email, SMS, etc.)

As an example, a core objective could be to avoid downtime, costly repairs and prevent injuries caused by equipment failure, and more. This real-time analysis works by enabling timely, informed decisions about when to schedule equipment maintenance, which in turn maximizes asset uptime, therefore increasing production output. In order to accomplish this, an organization must be able to ingest and process huge amounts of information from various disparate sources and cross correlate to provide accurate and timely analysis from all the data.

The numbers listed below are scalable (increase/decrease with the number of oil rigs, sensors and frequency).

• Number of rigs: 10,000
• Number of sensors per rig: 100
• Interval between measurements: 1 second

Total number of records loaded per second would equal 1 million. That would be 86 billion records per day or 32 trillion records per year. This equals 1.2 petabytes of data. Over a 5 year period, a company would need to load, archive, and analyze 160 trillion rows (6 petabytes)!

And, if they wanted to cross-correlate over longer periods of time for their forecasting, an organization could easily cross the 10 Petabyte threshold.

In this scenario, with MCAP, we would see loading speeds in excess of 50 million records per second (conservatively to take into account ‘spikes’).

Optimized queries on 6 Petabytes of sensor records would be less than 1 second!