As explored in a post last month, we detailed the fallout suffered by companies that experienced data breaches. The conclusion? The scope is expansive and has a long-term impact on brand value and customer trust. A new study takes that theory to the next level. Comparitech.com, a site that offers security and privacy advice, conducted a study of 24 companies that have experienced a data breach — comparing what happened to their stock after the breach. The result?
Based on the number of data breach reports in the news, it should be no surprise breaches in the U.S is on the rise. What is noteworthy, however, is the rate of which these breaches are occurring. New data from the Identity Theft Resource Center (ITRC) and CyberScout indicates as of June 30, 2017, the U.S. has seen a record high of 791 breaches — a 29 percent increase from 2016’s same timeframe. Based on this pace, the ITRC projects breaches to hit 1,500 this year alone.
FinTech advancements have transformed the banking industry in the past decade faster than ever before. Paving that path has been the ability of banks and credit unions to tackle one of their biggest problems (fraud) through one key trend: Machine learning. What's made that all possible? Software and options to integrate smarter, better fraud detection tools. While most of the chatter around how artificial intelligence (AI) will impact how banks and credit unions interact with their customers (i.e.
Our Co-Founder Canh Tran talked holiday card fraud with @JudyHsuABC7 in the studio today — but left a little time for some fun. Catch the segment (and Canh's tips to avoid card fraud) on Sunday morning. https://t.co/v3s4seNnYz
Muck Rack makes it simple to find people, tweets, or articles that mention any name, keyword, company, hashtag etc. We've compiled this guide to help you make the most of your search.
Selecting a term
Start searching tweets, articles from media outlets, articles mentioned in tweets, journalists'
names, titles and bios with some suggested searches:
Companies or Topics (e.g. iPhone, Microsoft)
Phrases (e.g. "cloud computing") — use quotes to keep the terms together
Twitter handles (e.g. @username) — returns those who have mentioned or replied to
Names (e.g. "David Pogue")
Hashtags (e.g. #sxsw, #london2012)
Bio details (e.g. vegan, Olympics, father)
Muck Rack's Advanced Search allows for many boolean operators.
Find results that mention multiple specified terms, use AND or
+. For example, ensure each result contains both Elon Musk and Mark Zuckerberg by
searching Musk AND Zuckerberg or Musk + Zuckerberg.
Use the operators OR or , to broaden your search when you'd like either of
multiple terms to appear in results. (This is the default behavior of our search when no operators
are used). For example, results will contain either cake or cookie by searching cake OR cookie or cake,cookie
Use NOT or - to subtract results from your search. For
example, searching Disney will yield results about the Walt Disney Company as well as Walt Disney
World Resort. To exclude mentions of Disney World, search for Disney -World or Disney
When using one of these operators with a phrase, enclose it in quotation marks. For example, you can
find results about smartphones excluding Apple's iPhone 4S by searching smartphone -"iPhone
Exact case matching or punctuation
If you're searching for a brand name or keyword that relies on specific punctuation marks or capitalization, you can
find results that match your exact query by adding matchcase: before the keyword you're searching for, like matchcase:E*TRADE .
Use parentheses to separate multiple
boolean phrases. For example, to find journalists talking about having fun in Disney World or
Disneyland, search for ("disney world" OR disneyland) AND fun.
An asterisk can be used to search for any variation of a root word truncated by the asterisk. For example, searching for admin* will return results for administrator, administration, administer, administered, etc.
A near operator is an AND operator where you can control the distance between the words. You can vary the distance the near operation uses by adding a forward slash and number (between 0-99) such as strawberries NEAR/10 "whipped cream", which means the strawberries must exist within 10 words of "whipped cream".