What are people currently doing to achieve security objectives for their organization? Where do those security objectives originate? Who are the people who are engaged in security and what are their reasons for engagement? Give an example of what may lead management to implement policies to change an organization culture relating to security?  

Require a PPT and APA formatted document. Use the RISK MANAGEMENT PLAN template to create the document and follow the Project template to create PPT

HLD is provided in Project Health Network Visual

Residency Written Assignment Instructions

Overview

As discussed in this course, risk management is an important process for all organizations. This is particularly true in information systems, which provides critical support for organizational missions. The heart of risk management is a formal risk management plan. The project activities described in this document allow you to fulfill the role of an employee participating in the risk management process in a specific business situation.

Submission Requirements 

All project submissions should follow this format: 

· Format: Microsoft Word 

· Cover page with all group members names

· Minimum 8 pages (not including reference page(s), or title page)

· Table of contents page

· Font: Arial, 10-point, double-space 

· APA Citation Style 

Scenario 

You are an information technology (IT) intern working for Health Network, Inc. (Health Network), a fictitious health services organization headquartered in Minneapolis, Minnesota. Health Network has over 600 employees throughout the organization and generates $500 million USD in annual revenue. The company has two additional locations in Portland, Oregon and Arlington, Virginia, which support a mix of corporate operations. Each corporate facility is located near a co-location data center, where production systems are located and managed by third-party data center hosting vendors.

Company Products 

Health Network has three main products: HNetExchange, HNetPay, and HNetConnect. 

HNetExchange is the primary source of revenue for the company. The service handles secure electronic medical messages that originate from its customers, such as large hospitals, which are then routed to receiving customers such as clinics.

HNetPay is a Web portal used by many of the company’s HNetExchange customers to support the management of secure payments and billing. The HNetPay Web portal, hosted at Health Network production sites, accepts various forms of payments and interacts with credit-card processing organizations much like a Web commerce shopping cart. 

HNetConnect is an online directory that lists doctors, clinics, and other medical facilities to allow Health Network customers to find the right type of care at the right locations. It contains doctors’ personal information, work addresses, medical certifications, and types of services that the doctors and clinics offer. Doctors are given credentials and are able to update the information in their profile. Health Network customers, which are the hospitals and clinics, connect to all three of the company’s products using HTTPS connections. Doctors and potential patients are able to make payments and update their profiles using Internet-accessible HTTPS Web sites.

Information Technology Infrastructure Overview 

Health Network operates in three production data centers that provide high availability across the company’s products. The data centers host about 1,000 production servers, and Health Network maintains 650 corporate laptops and company-issued mobile devices for its employees. 

Additional Information

– Server breakdown

o 100 Windows Server 2008 

o 900 Windows Server 2016

– Laptop breakdown

o 500 Windows 10

o 50 Windows XP

o 100 Linux

Deliverables

Graded Deliverable 1

As a group, develop the following:

1. A risk management plan to include the following sections:

1. Use the template provided

2. Must include all sections identified in the template

3. Must include at least 20 identified risks

4. Must include a list of at least 40 “mitigating” controls in place and identify what asset they apply to

5. Must write mitigation plans for the top 10 risks

Graded Deliverable 2

2. A presentation to include

1. A description of the top 5 risks you identified for Health Network

2. The reason why you ranked these as the top 5 risks

3. An explanation of each risk

4. The controls currently in place to mitigate the risk

5. The proposed mitigation plan to reduce the risk

Explain the Bresenham line Algorithm

YOUR TOPIC: You are the new Web Analyst for Friends of Disaster Relief (FDR) a non-profit organization. The company executives and the Marketing team cannot figure out why the organization is not receiving the expected targeted donations from the website. The website was created without any web tracking tools or a statistics capability, in other words no way to track visitors or anyone who made donations.

You have been called to the main conference room, to deliver your PowerPoint Presentation; your task is that you are trying to convince the top tier executives and marketing team that having a website without visitor tracking is not acceptable, you have developed a solution of why the organization should be using web analytics and what the organization should be tracking and suggesting the tools they should be using.

 The Presentation Guideline Requirements:

• Your presentation should consist of 10 -15 slides with an opening title slide.
• You must include notes to each slide in support of that page (this is very important). You should have 125 words on average or more per each slide notes.
• The notes must be supported by in text citations
• Your presentation must have references of 3 – 5 sources in total.
• Use should use graphics, themes and images to draw your audience into the presentation.

• What are mobile forensics and do you believe that they are different from computer forensics?
• What is the percentage of attacks on networks that come from mobile devices?
• What are challenges to mobile forensics?
• What are some mobile forensic tools?
• Should the analysis be different on iOS vs Android?

Your paper should meet the following requirements:

• Be approximately four to six pages in length, not including the required cover page and reference page.
• Follow APA7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
• Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources.
• Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.

Check Attachment

Enterprise Risk Management Integrating with Strategy and Performance Executive Summary. (2017, June). Retrieved from https://www.coso.org/Documents/2017-COSO-ERM-Integrating-with-Strategy-and-Performance-Executive-Summary.pdf

Do, H., Railwaywalla, M., & Thayer, J. (2016). Integration of ERM with Strategy (p. 35). Retrieved from Poole College of Management, NCSU website: https://erm.ncsu.edu/az/erm/i/chan/library/Integration_of_ERM_and_Strategy_Case_Study.pdf

fter reading the main articles this week, and any other relevant research you locate, please discuss the following in your main post:.

• Which case study in the paper was most interesting to you and why?
• Do you think that ERM is necessary in the contemporary organization and why?

Write at least 500 words on fractional ownership and it’s relation to cloud computing. Use at least one example from another industry.    

Use at least three sources. Include at least 3 quotes from your sources enclosed in quotation marks and cited in-line by reference to your reference list.  Example: “words you copied” (citation) These quotes should be one full sentence not altered or paraphrased. Cite your sources using APA format. Use the quotes in your paragaphs. Stand alone quotes will not count toward the 3 required quotes.

Copying without attribution or the use of spinbot or other word substitution software will result in a grade of 0. 

Write in essay format not in bulleted, numbered or other list format. 

Do not use attachments as a submission. 

Do not double space.

Reply to two classmates’ posting in a paragraph of at least five sentences by asking questions, reflecting on your own experience, challenging assumptions, pointing out something new you learned, offering suggestions. These peer responses are not ‘attaboys’.   You should make your initial post by Thursday evening so your classmates have an opportunity to respond before Sunday.at midnight when all three posts are due. 

It is important that you use your own words, that you cite your sources, that you comply with the instructions regarding length of your post and that you reply to two classmates in a substantive way (not ‘nice post’ or the like).  Your goal is to help your colleagues write better. Do not use spinbot or other word replacement software. It usually results in nonsense and is not a good way to learn anything. . I will not spend a lot of my time trying to decipher nonsense. Proof read your work or have it edited. Find something interesting and/or relevant to your work to write about.  Please do not submit attachments unless requested.

Topic:: Cloud migration and data security as key indicator in e-commerce and business success

Identify top 5 journals related that topic and write 150 words about each one.

No plagiarism 

 There are many ways to misrepresent data through visualizations of data. There are a variety of websites that exist solely to put these types of graphics on display, to discredit otherwise somewhat credible sources. Leo (2019), an employee of The Economist, wrote an article about the mistakes found within the magazine she works for. Misrepresentations were the topic of Sosulski (2016) in her blog. This is discussed in the course textbook, as well (Kirk, 2016, p. 305).

After reading through these references use the data attached to this forum to create two visualizations in R depicting the same information. In one, create a subtle misrepresentation of the data. In the other remove the misrepresentation. Add static images of the two visualizations to your post. Provide your interpretations of each visualization along with the programming code you used to create the plots. Do not attach anything to the forum: insert images as shown and enter the programming code in your post.

When adding images to the discussion board, use the insert image icon.

This is the data to use for this post: Country_Data.csv

Before plotting, you must subset, group, or summarize this data into a much smaller set of points. Include your programming code for all programming work. It would be more likely that one would win a multi-million dollar lottery than plot the same information the same exact way. However, if you have, you will need to repost and make your post unique. The first post to provide the content does not need to change.

References

Kirk, A. (2016). Data visualisation: A handbook for data driven design. Sage.

Leo, S. (2019, May 27). Mistakes, we’ve drawn a few: Learning from our errors in data visualization. The Economist. https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368

Sosulski, K. (2016, January). Top 5 visualization errors [Blog]. http://www.kristensosulski.com/2016/01/top-5-data-visualization-errors/

An example post:

The factual and misrepresented plots in this post are under the context that the visualizations represent the strength of the economy in five Asian countries: Japan, Israel, and Singapore, South Korea, and Oman. The gross domestic product is the amount of product throughput. GDP per capita is the manner in which the health of the economy can be represented.

The visual is provided to access the following research question:

How does the health of the economy between five Asian countries: Japan, Israel, and Singapore, South Korea, and Oman, compare from 1952 to 2011?

The plot on the left is the true representation of the economic health over the years of the presented countries. Japan consistently has seen the best economic health of the depicted countries. Singapore and South Korea both have large increases over the years, accelerating faster than the other countries in economic health. Oman saw significant growth in the years between 1960 and 1970, but the growth tapered off. All of the countries saw an increase in health over the provided time frame, per this dataset. Israel saw growth, but not as much as the other countries.

The plot on the right is only GDP and does not actually represent the economic health. Without acknowledging the number of persons the GDP represents, Japan is still the leading country over the time frame and within the scope of this dataset. Singapore’s metrics depict some of the larger issues of representing the GDP without considering the population. Instead of Singapore’s metrics depicting significant growth and having a level of health competitive with Japan in the true representation, Singapore has the fourth smallest GDP. It indicates that Singapore’s economy is one of the least healthy amongst the five countries.

The programming used in R to subset, create, and save the plots:

# make two plots of the same information - one misrepresenting the data and one that does not

# use Country_Data.csv data

# plots based on the assumption the information is provided to represent the health of the countries' economy compared to other countries

# August 2020

# Dr. McClure

library(tidyverse)

library(funModeling)

library(ggthemes)



# collect the data file

pData <- read.csv("C:/Users/fraup/Google Drive/UCumberlands/ITS 530/Code/_data/Country_Data.csv")

# check the general health of the data

df_status(pData)

# no NA's no zeros



# look at the data structure

glimpse(pData) # nothing of note

# arbitrarily selected Asia, then list the countries by the highest gdp per capita, to plot competing economies*

# select countries - also use countries that cover all of the years in the dataset (52 years)

(selCountries <- pdata %>% 

    filter(continent == "Asia") %>%

    group_by(country) %>%

    summarise(ct = n(),

              gdpPop = mean(gross_domestic_product/population)) %>%

    arrange(-ct, 

            -gdpPop) %>%

    select(country) %>%

    unlist())

# many countries have 52 years worth of data

# good plot representation of the GDP per capita

p1 <- pdata %>% 

    filter(country %in% selCountries[1:5]) %>%    # use subset to identify the top 5 countries to filter for

    ggplot(aes(x = year,                          # plot the countries for each year

               y = log(gross_domestic_product/population), # calculating the log of gdp/pop = GDP per capita

               color = country)) +                # color by country

    geom_line() +                                 # creating a line plot

    scale_x_continuous(expand = expansion(add = c(7,1)), # expand the x axis, so the name labels of the country are on the plot

                       name = "Year") +           # capitalize the x label, so the annotation is consistent

    geom_text(inherit.aes = F,                    # don't use the aes established in ggplot

                        data = filter(pData,                 # filter for one data point per country for the label, so one label per country

                           country %in% selCountries[1:5],

                           year == 1960),

             aes(label = country,                 # assign the label

                 x = year,

                 y = log(gross_domestic_product/population), # keep the axes and color the same

             color = country),

             hjust = "outward",                   # shift the text outward

             size = 3) +                          # make the text size smaller

    scale_color_viridis_d(end = .8,               # don't include the light yellow, not very visible

                          guide = "none") +       # no legend, because of text labels

    scale_y_continuous(name = "GDP per capita - Log Scale") +      # rename y axis

    ggtitle("Five Asian Countries: GDP per Capita between 1960 and 2011") +      # plot title

    theme_tufte()

# misrepresent economic health - don't account for population

p2 <- pdata %>% 

    filter(country %in% selCountries[1:5]) %>%    # use subset to identify the top 5 countries to filter for

    ggplot(aes(x = year,                          # plot the countries for each year

               y = log(gross_domestic_product),   # calculating the log of gdp

               color = country)) +                # color by country

    geom_line() +                                 # creating a line plot

    scale_x_continuous(expand = expansion(add = c(7,1)), # expand the x axis, so the name labels of the country are on the plot

                       name = "Year") +           # capitalize the x label, so the annotation is consistent

    geom_text(inherit.aes = F,                    # don't use the aes established in ggplot

                        data = filter(pData,                 # filter for one data point per country for the label, so one label per country

                           country %in% selCountries[1:5],

                           year == 1960),

             aes(label = country,                 # assign the label

                 x = year,

                 y = log(gross_domestic_product), # keep the axes and color the same

             color = country),

             hjust = "outward",                   # shift the text outward

             size = 3) +                          # make the text size smaller

    scale_color_viridis_d(end = .8,               # don't include the light yellow, not very visible

                          guide = "none") +       # no legend, because of text labels

    scale_y_continuous(name = "GDP - Log Scale") +      # rename y axis

    ggtitle("Five Asian Countries: GDP between 1960 and 2011") +      # plot title

    theme_tufte()

# save each plot with a transparent background in the archive image folder 

ggsave(filename = "PerCapita.png",

      plot = p1,

      bg = "transparent",

      path = "./code archive/_images")

ggsave(filename = "GDP.png", 

      plot = p2,

      bg = "transparent",

      path = "./code archive/_images")