Protecting Essential Data with Security Analytics

Every day 2.5 quintillion bytes of data are produced. Some of it is important (and private), like the most essential of financial and medical records. Some of it, like the cleaning path taken by an IoT vacuum is largely useless (or is it?). And all of it can become vulnerable without the right security effort.

Subsequently, there is a growing need for us to find new and better ways to protect our most sensitive data from a host of digital threats rise. Cybersecurity analytics can ultimately help to lay the foundation for large scale data protection.  Some questions that this post sets out to answer include:

What is cybersecurity analytics?

What are the benefits of cybersecurity analytics?

What are some common cybersecurity use cases?

How do AI and cybersecurity analytics go together?

What are some best practices for cybersecurity analytics?

What are some pitfalls/challenges in cybersecurity analytics?

What are tools to aid in effective cybersecurity analytics?

How can I start to establish a cybersecurity analytics program in my organization?

What is Cybersecurity Analytics?

Every day millions of cyber attacks are successfully executed around the world. Though it may be impossible to 100% prevent them all, we can certainly learn from them to help develop better ways to stay protected. Cybersecurity analytics studies the digital trail left behind by cyber criminals to help better understand weaknesses and how to prevent similar losses in the future.

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Exploring Analytics & AI in 2018: A Detailed Primer

It is often said that data is the most valuable asset a business can have; the oil of a digital era. But data itself, while interesting, often leaves out a variety of important details – creating a need for analytics. And how we complete these analytics has evolved – and will continue to do so; particularly with the rapid proliferation of AI tools and technologies.

Some benefits associated with AI and analytics include:

  • Increased productivity via instant access to the right data through better classification processes
  • Improved speed, accuracy, and efficiency throughout analytics and data management practices
  • Facilitating clear compliance with legal and similar requirements
  • Cost saving by finding the best ways to achieve a wide range of tasks

To better understand data, analytics, AI, and how they all go together, we will answer the following questions: Read more

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Workforce Analytics: A 2018 Introduction for Employers & Businesses

Employees are one of the biggest investments that an organization can make, so it’s not surprising that the field of workforce analytics has emerged to help support it. And with the amount of data that is being generated and recorded about not just employees, but the organization as a whole, the time is ripe for analysis. This is becoming even more true as our working population expands to include alternative forms of employees in chatbots and similar. 

In this post we will introduce workforce analytics by answering the following questions: Read more

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Pratik Jain from morph.ai on how bots transform marketing

This episode of AppliedAI is on an AI vendor for product marketing that specifically focuses on solving issues in critical metrics like lead quality, engagement, drop-offs, user retention, return rates. We hosted Pratik Jain, co-founder of Morph.ai. Morph.ai is leveraging chat to help marketers solve inefficiencies in the marketing process for large enterprises. Pratik was one of the early hires of Sprinklr, a social media SaaS tool which grew from 200 million to 2 Billion valuation while Pratik was part of the team. After Sprinklr, Pratik and a few of his colleagues from Sprinklr founded Morph.ai which initially began as an open platform and supported ~2300 bots. 2 year down the road, they now have a closed, enterprise only platform serving more than 20 enterprises. Pratik explains that the rapid growth  is a result of focusing on one single usecase in marketing that is valuable for corporations and consciously ignoring the glamor. Their focus areas are lead generation and engagement on Facebook. They have seen great success with their workflows which is a mechanism for following up with customers that have not converted as leads. The approach has been successful also because older approaches for engaging drop-offs such as emails and calls are not effective. Email open rates are low and calls are annoying and expensive. Chat is a seamless, real-time way to engage customers that have indicated interest but not yet become leads. A carefully placed series of followup messages is their unique selling point. Their implementation can be set up in 2 weeks even for an enterprise if the customer can prepare everything they need upfront. To launch, they need:
  • 4-8 hours of the marketing responsible to identify current issues
  • Some images and videos to use in their chat campaigns
  • Approvals such as operations and security teams’ approvals which becomes the main bottleneck in most cases.
Most of the time their enterprise implementations can take 3-4 months as it takes time to get all approvals and finish all the integrations. They are working on improving how they convey marketing insights so companies can directly get these insights. Currently, analysts are helping brands understand these insights. They also plan to expand into other geographies including UK and the customers’ demands in new geographies will help determine their product roadmap. With half the world’s population on chat, Pratik believes that chat will be a channel at least as important as social media in the next few years. And brands need a chat marketing strategy.
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Synthetic Data: An Introduction & 10 Tools [07/2018 Update]

Synthetic data, as the name suggests, is data that is artificially created rather than being generated by actual events. It is often created with the help of algorithms and is used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI needs.

The questions that this post sets out to answer include:

Why is synthetic data important and what are some use cases for it?

How does synthetic data perform compared to real data?

What are some benefits associated with synthetic data?

What are some basics of synthetic data creation?

What are some challenges associated with synthetic data?

What are some tools related to synthetic data?

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