Think of all the ways you could be a victim of a crime. Do you classify any of these methods as inevitable? In almost every scenario, it is not even close to inevitability, with one major exception: Identity theft.
Your data may already be exposed online.
The Collection 1 dump of 773 million email addresses and 22 million passwords, was part of a much larger set of databases containing over 3.5 billion user records and placed online. The largest public data breach by volume shows that you can not be too careful with your online records.
But do we care about our online data?
Plantation, Fla.-based SaaS access control company ERP Maestro surveyed 2,000 Americans ages between 18 and 82 in December 2018. It wanted to find out about their experiences and perception of cybercrime and identity theft
It discovered that three out of four Americans (76 percent) believe that it is inevitable that they will be victims of identity theft or cybercrime. However, almost half (48 percent) of respondents said that they are not concerned about it.
Over one in three (37 percent) of Americans say that they have already been victims of cybercrime and identity theft. Many of these have fallen victim to credit or debit charges (64 percent), stolen information (16 percent), or employment or tax fraud (6 percent)
The top states with prevalence of identity theft are: 1. Michigan, 2. California, 3. Florida, 4. Maryland, and 5. Nevada. West Virginia, and South Dakota have the least amount of identity theft.
Sadly, only 32 percent of victims think they could have prevented the crime, and three out of four victims said that they did not change their online behaviour after the identity theft or cyber crime happened.
Almost half (48 percent) thought that the incident was a big issue, causing them a lot of time, while others complained of losing a little money (19 percent), damaged credit ratings (11 percent), losing a lot of money (10 percent), or hurting their reputation (4 percent).
One in three felt there were no real consequences from the data compromise.
To prevent identity theft or cyber crime, almost four out of five (77 percent) of Americans look for fraudulent debit or credit charges on their account. Three out of five (60 percent) use very complex passwords, and 55 percent use firewalls or anti virus software at home.
Almost three out of four said that high profile data breaches –- such as Facebook, Twitter, and Marriot breaches in 2018 — have caused them to change their online behaviour.
Over half of Americans (57 percent) believe if something happens, the damage will just get reversed, and less than one in 10 (9 percent) carry out the necessary steps to prevent cash disappearing from their accounts by freezing inactive credit cards.
Both businesses and consumers need to assess their security risk and take all steps possible to prevent identity theft happening. Complacency will lead to ever larger data breaches — until no one’s data is safe.
Previous and related coverage:
Who is really in the driver’s seat? Unknown digital threats to your car’s security
The technology managing the systems in our cars could be open drivers up to the risk of hacking. Find out the most common digital threats to our cars, and how you can reduce your risk.
Two thirds of US consumers say Government should do more to protect data privacy
In the wake of breaches, US consumers are concerned more than ever about their data.
Four out of five Americans distrust mainstream social media sites like Facebook
Americans are so disillusioned by social media, that many are turning to forums for trustworthy information.
Betrayal by CGI: Almost half of Gen Y and Z don’t know they’re following a bot
What if the influencer you had been following on Instagram — whose choices you admired, and whose social causes that you believed in — turned out to be a robot?
Cloud Data Security
Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.
The requirements have grown well beyond the simplistic features provided by data platforms, and as a result a competitive industry has emerged to address the security layer. The capabilities of this layer must be more than thorough, they must also be usable and streamlined, adding a minimum of overhead to existing processes.
To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta. This study contrasts the differences between a largely role-based access control model with object tagging (OT-RBAC) to a pure attribute-based access control (ABAC) model using these respective technologies.
This study captures the time and effort involved in managing the ever-evolving access control policies at a modern data-driven enterprise. With this study, we show the impacts of data access control policy management in terms of:
- Dynamic versus static
In our scenarios, Ranger alone took 76x more policy changes than Immuta to accomplish the same data security objectives, while Ranger with Apache Atlas took 63x more policy changes. For our advanced use cases, Immuta only required one policy change each, while Ranger was not able to fulfill the data security requirement at all.
This study exposed the limitations of extending legacy Hadoop security components into cloud use cases. Apache Ranger uses static policies in an OT-RBAC model for the Hadoop ecosystem with very limited support for attributes. The difference between it and Immuta’s attribute-based access control model (ABAC) became clear. By leveraging dynamic variables, nested attributes, and global row-level policies and row-level security, Immuta can be quickly implemented and updated in comparison with Ranger.
Using Ranger as a data security mechanism creates a high policy-management burden compared to Immuta, as organizations migrate and expand cloud data use—which is shown here to provide scalability, clarity, and evolvability in a complex enterprise’s data security and governance needs.
The chart in Figure 1 reveals the difference in cumulative policy changes required for each platform configuration.
Figure 1. Difference in Cumulative Policy Changes
The assessment and scoring rubric and methodology is detailed in the report. We leave the issue of fairness for the reader to determine. We strongly encourage you, as the reader, to discern for yourself what is of value. We hope this report is informative and helpful in uncovering some of the challenges and nuances of data governance platform selection. You are encouraged to compile your own representative use cases and workflows and review these platforms in a way that is applicable to your requirements.
GigaOm Radar for Data Loss Prevention
Data is at the core of modern business: It is our intellectual property, the lifeblood of our interactions with our employees, partners, and customers, and a true business asset. But in a world of increasingly distributed workforces, a growing threat from cybercriminals and bad actors, and ever more stringent regulation, our data is at risk and the impact of losing it, or losing access to it, can be catastrophic.
With this in mind, ensuring a strong data management and security strategy must be high on the agenda of any modern enterprise. Security of our data has to be a primary concern. Ensuring we know how, why, and where our data is used is crucial, as is the need to be sure that data does not leave the organization without appropriate checks and balances.
Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. People and processes are key, as, of course, is technology in any data loss prevention (DLP) strategy.
This has led to a reevaluation of both technology and approach to DLP; a recognition that we must evolve an approach that is holistic, intelligent, and able to apply context to our data usage. DLP must form part of a broader risk management strategy.
Within this report, we evaluate the leading vendors who are offering solutions that can form part of your DLP strategy—tools that understand data as well as evaluate insider risk to help mitigate the threat of data loss. This report aims to give enterprise decision-makers an overview of how these offerings can be a part of a wider data security approach.
Key Criteria for Evaluating Data Loss Prevention Platforms
Data is a crucial asset for modern businesses and has to be protected in the same way as any other corporate asset, with diligence and care. Loss of data can have catastrophic effects, from reputational damage to significant fines for breaking increasingly stringent regulations.
While the risk of data loss is not new, the landscape we operate in is evolving rapidly. Data can leave data centers in many ways, whether accidental or malicious. The routes for exfiltration also continue to grow, ranging from email, USB sticks, and laptops to ever-more-widely-adopted cloud applications, collaboration tools, and mobile devices. This is driving a resurgence in the enterprise’s need to ensure that no data leaves the organization without appropriate checks and balances in place.
Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. Policy, people, and technology are critical components in a data loss prevention (DLP) strategy.
As with any information security strategy, technology plays a significant role. DLP technology has traditionally played a part in helping organizations to mitigate some of the risks of uncontrolled data exfiltration. However, both the technology and threat landscape have shifted significantly, which has led to a reevaluation of DLP tools and strategy.
The modern approach to the challenge needs to be holistic and intelligent, capable of applying context to data usage by building a broader understanding of what the data is, who is using it, and why. Systems in place must also be able to learn when user activity should be classified as unusual so they can better interpret signs of a potential breach.
This advanced approach is also driving new ways of defining the discipline of data loss prevention. Dealing with these risks cannot be viewed in isolation; rather, it must be part of a wider insider risk-management strategy.
Stopping the loss of data, accidental or otherwise, is no small task. This GigaOM Key Criteria Report details DLP solutions and identifies key criteria and evaluation metrics for selecting such a solution. The corresponding GigOm Radar Report identifies vendors and products in this sector that excel. Together, these reports will give decision-makers an overview of the market to help them evaluate existing platforms and decide where to invest.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.
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