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Why Do We Need More Security In Healthcare Network

WHY WE NEED MORE SECURITY IN HEALTHCARE NETWORKE

Undoubtedly, the Internet of Things (IoT) and related services have revolutionized the healthcare industry. There are many examples for this, i.e. it is possible to directly access patient records, provide remote diagnostics, and provide appropriate treatment options. There are also lifecycle management and apps for monitoring vital signs.

 

Two motifs are particularly driving growth in this segment. Firstly, the offered services should become more efficient and, secondly, they should offer its users better treatment quality. The so-called “connected health” market has grown so fast that experts predict that it will reach a volume of around 612 billion US dollars by 2024.

 

The number of IOT devices is growing continuously. However, this also has its weaknesses, because at the same time it increases the number of cyberattacks. According to the results of the 2018 Thales Data Threat Report, 77% of US healthcare organizations have been breached, along with two out of five respondents in the healthcare industry worldwide who claimed to have been victims of a data breach last year. Personal medical devices, monitoring devices that can be used at home, and similar applications, certainly have both health and commercial benefits. However, the main question is if we cannot even protect 100% a single network boundary, how would it be possible to secure 1,0000000 or even more “mobile” perimeters?

 

healthcare thalese security

 

The healthcare industry contains many sub-industries that already work together in an ecosystem, but they need to strengthen their work relation and need new strategies in protection of personal health information (PHI). These sub-industries ensure the security of systems that deliver patient care. Here’s a look at the role each type of organization plays in the healthcare ecosystem as it relates to software security:

 

healthcare ecosystem

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Given the potential value of the personal information held on or exchanged between these devices, it is not surprising that healthcare is one of the industry most likely to be victims of cyber-attacks. Certain patient records contain little more than the necessary basic information on the treatment history of a patient. We can face a bit different situation with the US Electronic Health Records (EHR). Not only are these records much more detailed but they also contain other valuable data such as credit card and social security numbers. The electronic health record is also a database in which treatment data, medicines, allergies and other health data of the health insurance are to be regularly stored across all sectors and cases nationwide. Depending on which model is used, the data is stored in centralized or decentralized database. However, participation is initially voluntary, and the patient should be allowed to decide on the type and extent of storage.

 

The theft of patient’s personal information can have traumatic consequences. Under certain circumstances, human lives are at stake here. The consequences of a data breach are hard to imagine. A striking example from the recent past. One can only guess that what would have happened if the FDA had not recalled 465,000 networked pacemakers due to hacking fear. Last year, users were advised to bring in a patch designed to fix a potential vulnerability in their devices.

 

With regard to medical data, it is absolutely necessary that the correct data must be correctly transmitted to the right device. And then the right treatment processes are triggered, and medication are precisely implemented, of course, with the right patient.

 

Patients and healthcare professionals alike demand a certain level of security when implementing such devices. The devices themselves must be as secure as any data and information they transmit and share. Encryption and secure key management, for example, are essential in ensuring the confidentiality of data stored and shared between medical networked devices.

 

Recently, Thales has partnered with Device Authority in order to protect healthcare IoT. The solution authenticates each new device hardware and establishes a strong root of trust and identity within the network. End-to-end encryption ensures the integrity of the data in question.

 

Cutting costs, working effectively, increasing motivation, adopting a healthier lifestyle, and reducing error margins are the mottos of each health providers. It is beyond argue that innovative technical applications have meanwhile provided better services to patients. However, an overabundance of new opportunities and uses has also opened up new opportunities for cybercriminals and increased the risk of data breaches. Unlike many other data breaches, health care is very quickly about the lives of patients and at least highly confidential data. Healthcare providers should do their utmost to limit such risks as much as possible and to ensure that patients and other stakeholders can trust each device.

GDPR: Artificial Intelligences’ Major Blockage

The data protection and privacy law, which came into effect across the EU on 25thmay have a great impact on companies building machine learning systems. We know that in order to build these systems, companies’ needs large amount of data, but Big data is completely opposed to the basis of data protection.

 

According to the EU Data Protection Regulation, companies must meet three specified transparency requirements (along with other suitable safeguards) in order to better inform data subjects about the Article 22 (1) type of processing and the consequences:

 

  • inform the data subject purpose of data storage;
  • provide meaningful information about the logic involved; and
  • explain the significance and envisaged consequences of the processing.

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The logic behind is to be aware of how far this transparency provision is interpreted and whether companies have to fear or not.

 

AI is omnipresent: From the analysis of large and complex data sets such as genome data in medical research, Predictive Policing in the police and security sector to digital language assistants such as Apple’s Siri or Alexa of Amazon. Even fitness apps are increasingly relying on the use of AI and machine learning in order to be able to offer each user a tailor-made training plan optimized for them.

 

This trend has not gone unnoticed by politicians. After the European Commission presented a European concept in the field of artificial intelligence at the end of April, the parliamentary groups also took part on 26 June 2018 , to discuss over recommendations for action in the handling of artificial intelligence – especially in legal and ethical terms – by the summer break in 2020. The AI ​​concept of the European Commission also provides extensive research and development measures with the aim of promoting AI innovation in Europe.

 

Even if the use of AI does not necessarily have to be associated with the evaluation of personal data in every case of application, for example in banking and insurance, it also stays suitable for the comprehensive evaluation of personality traits (so-called “profiling” / “scoring”). According to European data protection authorities, an example of profiling and classifying is the following:

 

“a business may wish to classify its customers according to their age or gender for statistical purposes and to acquire an aggregated overview of its clients without making any predictions or drawing any conclusion about an individual. In this case, the purpose is not assessing individual characteristics and is therefore not profiling.”

 

It is therefore astonishing that the concepts of the EU Commission with regard to the data protection measurement of AI use have so far remained rather vague for companies.

 

Regardless of the admissibility of a particular procedure, these transparency obligations are often seen as extremely critical in the light of the protection of trade and business secrets. The reason for this is that the person concerned must also be provided with “meaningful information about the logic involved” and it is still unclear to what extent and to what amount this information is to be given. The key question is whether the person in charge, ie the company using the AI, is only required to explain the principles and essential elements underlying an automated decision-making process descriptively, or whether the disclosure of calculation formulas, parameters and algorithms can actually be demanded from this.

 

In any case, with the view expressed here, there is no obligation to disclose formulas and algorithms from the GDPR. The transparency provisions of the GDPR therefore only require “meaningful information about the logic involved” of automated decision-making, but not the actual publication of these logics. According to this, the responsible party owes only a description of the principles underlying an automated decision-making process, that is to say about the fundamental laws by which an algorithm makes decisions. The purpose of the GDPR obligations is therefore not (as often represented) to enable the concerned person to recalculate the results of an automated decision-making process, for example the “score” of the concerned person. This would require, for example, the specific calculation formula and the calculation parameters. Rather, in the context of the transparency provisions, for example in the context of a privacy policy, the data subject should only be given the opportunity to obtain advance information on the extent to which his data is processed by a particular service provider and, if appropriate, to look for alternatives.

 

This view is not contradicted by the requirement of “meaningfulness” of the required information. On the other side, for the average user, a comprehensible description of the underlying processes may represent a greater added value than the disclosure of the mathematical-technical logics themselves. Only by then a generally understandable description can meet the requirements of the GDPR. This requires that all information to be provided must be provided in an intelligible form and in a “clear and simple language”.

 

In summary, the GDPR lurks no real danger for the protection of know-how. Rather, their admissibility requirements and transparency obligations in the use of automated decision-making are consistent and appropriate: Human individuals should not become the ordinary “ball” of machines. If machines make automated decisions without being checked by professionals for precision, it can lead to insignificant results as well.

From Good Ideas to Effective Innovation Management

Innovation Cycle Stages

‘Innovation … is generally understood as the introduction of a new thing or method … Innovation is the embodiment, combination or synthesis of knowledge in original, relevant, valued new products, processes or services.’  Luecke and Katz, 2003

 

Creativity is often seen as the basis for innovation. For innovation to occur, there needs to be a creative idea and the ability to convert that idea into action to make a difference. The result is a specific and tangible change in the products, services or business processes provided by an organization: ‘All innovation begins with creative ideas . . . we define innovation as the successful implementation of creative ideas within an organization. In this view, creativity by individuals and teams is a starting point for innovation; the first is a necessary but not sufficient condition for the second.’  Amabile et al, 1996

 

Innovation is vital for business survival in today’s highly competitive markets where it is increasingly difficult to differentiate products and services. Innovation is important for the following reasons:

  • it allows businesses to expand their customer base by refreshing the market with new and improved products
  • it is a key component of competitive advantage and helps companies stay ahead of competitors before competitors’ innovations take market share
  • it provides incremental revenue and profit and also increases shareholder value.

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In order to generate innovative products and services from ideas, a targeted innovation management is required. Businesses that are not growing through new product and service introduction are likely to decline as their existing sales portfolio matures. It is not surprising that companies such as Procter & Gamble and General Electric have actively embraced the management of innovation. Their principal goal is to drive growth and then to improve shareholder value.

Therefore, innovation management must be implemented strategically and established in order to successfully implement and thus monetize good ideas. An MVP (Minimum Viable Product) alone is not sufficient without a corresponding MVO – Minimum Viable Organization.

 

Purposeful, effective, and scalable innovation management enables a business to gain significant competitive advantage while improving customer loyalty and satisfaction. Coupling agile transformation not only helps meet high expectations of speed of implementation, but also provides synergy effects in both directions.

 

Here below are listed the four Best Practices from CGI Germany, which are crucial to success.

 

  1. Setup of Innovation Management:

The internal “Innovation Management Team” must set up the necessary processes, create the organizational structure for implementation and run successful internal marketing. This includes, for example, establishing the required roles and committees, such as an evaluation board, the possible implementation of a suitable IT tool for the management of the ideas and the portfolio, setting up the structures for ongoing communication and the selection of the innovation methods to be applied. This includes accompanying measures to promote the culture of innovation. Having your own budget is imperative – and above all embedding in the corporate strategy and access to influential and recognized sponsors. Sometimes it is also necessary to consolidate various innovation approaches in the company, thus exploiting synergy effects and achieving an even stronger focus on the common strategy. An external consultant can bring in valuable experience and know-how, which are adapted to the company.

 

  1. Ongoing Innovation Service:

Every single innovation campaign and every innovation project has to be prepared, carried out and supervised. Often, the internal team lacks manpower, so important issues cannot be addressed due to limited resources. Here, an external service provider can provide valuable support by, for example, taking over complete campaigns or projects as required or even providing only partial services such as the planning and implementation of design thinking workshops. He can also contribute important experience to typically critical issues such as conducting a good evaluation. Overall, it is important for the ongoing Innovation Service to work on a continuous improvement of the overall process.

 

  1. Support of innovators:

Some innovators have brilliant ideas, but unfortunately, they are never implemented because they cannot represent the value of the idea. In part, the company’s internal barriers, such as administration or access to resources, are far too high and demoralizing. Here, a pool of internal or external “Innovation Guides” and subject matter experts helps to support the innovators.

 

  1. Culture Shift Support:

Last but not least, it is also the task of a good innovation management to prepare the breeding ground in the company so that an innovation-friendly climate can emerge. This includes accompanying communication, workshops, community building, culture hacking, brainstorming and promoting collaboration across areas. Experienced external service providers bring along a well-filled method case for specific measures of a viral approach.

 

“Innovation management without digital transformation is possible, but not digital transformation without innovation management. In order to compete and provide customer-focused services fast enough, many companies rely on an external service provider. Innovation Management is not a one-time project but involves a continuous change in the mindset and has a high strategic importance in times of digital transformation “says Andrea Schmitz, Director at CGI in Munich.

#GDPR: How Enterprises Can Ensure GDPR Compliance in Cloud Industry

With the Global Data Protection Regulation (GDPR) on the horizon, businesses operating in the EU will have to think, more than before, about compliance.The GDPR causes uncertainty within company’s management, because they are often unclear about whether they store personal data and, if so, where these are. GDPR is a legislative challenge that business must have to overcome.

 

May 25, 2018 is over, the EU General Data Protection Regulation is in highlight and companies are reworking their strategies towards its adoption. Any company that collects or processes personal data of EU citizens must comply with the obligations of the new law. Many still encounter new stumbling blocks on their way to conformity.

 

Here below are four simple steps for companies to consider all personal data collections, regardless of platform.

 

 

  1. Set up automatic data discovery in local and cloud environments

Without comprehensive data inventory, GDPR compliance is virtually impossible. Automated discovery solutions can help keep your collections of data up-to-date – especially when adding or removing new systems on-premise or in the cloud. They search thousands of applications. They are able to identify SaaS solutions that store or process personal information. They can filter the data and are able to generate specific views depending on what the company is most interested in. This is how companies make sure that nothing goes wrong.

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  1. Determine what data is shared with suppliers and how they handle it

One of the most complex requirements of the GDPR: organizations are not only responsible for appropriate security measures in their own environment. You must also ensure that your customers’ personal information is safe from vendors with whom they share it. Many people in charge share personal information about SaaS applications with processors. Only those who know their SaaS data accurately can identify the providers who process this personal data – and fulfill their responsibilities adequately.

 

  1. Categorize personal information and know where it is

Many GDPR processes require companies to know not only where personal data is located, but also what kind of personal information it is. For example, to implement the “right to be forgotten”, companies must be able to locate the subject’s personal data and then filter out which data needs to be deleted and which data must be retained.

 

  1. Regulate access to personal data

With localized databases, most businesses do a good job of maintaining access controls. However, as with automatic detection solutions, these controls are eliminated in SaaS-based personal data collections. Many companies rely on simplified access control hierarchies here. However, these provide users with far-reaching insights into their personal data. Making access to all personal data, including the SaaS-based repositories, visible and controllable is an important step towards GDPR compliance.

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