Risk Analysis in Business Intelligence & Artificial Intelligence

Risk Analysis in a nutshell;

Risk analysis is the process of identifying and analyzing potential issues that could negatively impact key business initiatives or projects. This process is done with the assistance of AI Technology (Artificial Intelligence) in order to help organisations avoid or mitigate those risks based on their quantum data they've received from these system assessments.

Performing an intelligent risk analysis includes considering the possibility of adverse events caused by either natural processes, like severe storms, earthquakes or floods, or adverse events caused by malicious or inadvertent human activities. An important part of risk analysis is identifying the potential for harm from these events, as well as the likelihood that they will occur.

Organisations use intelligent risk analysis to:

• Anticipate and reduce the effect of harmful results from adverse events;

• evaluate whether the potential risks of a project are balanced by its benefits to aid in the decision process when evaluating whether to move forward with the project;

• plan effective and intelligent responses for technology or equipment failure or loss from adverse events, both natural and human-caused; and

• identify the systems impact of and prepare for changes in the enterprise environment and adjust business intelligence per it, including the likelihood of new competitors entering the market or changes to government regulatory policy.

Benefits of Quantum Risk Analysis by PISIQ

Organisations must understand the possible risks associated with the use of their peripheral information to effectively and efficiently protect their information assets.

Risk analysis can help an organization improve its security (Enhanced Security) in a number of ways utilising AI Technology (Artificial Intelligence) for their systems. Depending on the type and extent of the risk analysis, organisations can use the results to help:

• Identify, rate and compare the overall impact of risks to the organisation, in terms of both financial and organisational impacts;

• Identify gaps in security and determine the next steps using machine learning to eliminate the weaknesses and strengthen enhanced security;

• Enhance communication and intelligent decision-making processes based on the business intelligence as they relate to information security;

• Improve security policies and procedures and develop cost-effective methods for implementing these information security policies and procedures;

• Put security control systems in place to mitigate the most important risks;

• Increase employee awareness about security measures and risks by highlighting best practices during the risk analysis process; and

• Understand the financial impacts of potential enhanced security risks.

Done well, risk analysis is an important tool for managing costs associated with risks, as well as for aiding an organization's decision-making process.

Steps in risk analysis systems process

The risk analysis process usually follows these basic steps:

1. Conduct a risk assessment survey: This first step, getting input from AI and human management and department heads, is critical to the risk assessment process. The risk assessment survey is a way to begin documenting specific risks or threats within each department.

2. Identify the risks: The reason for performing risk assessment is to evaluate an IT system or other aspect of the organization and then ask: What are the risks to the software, hardware, quantum data and IT employees? What are the possible adverse events that could occur, such as human error, fire, flooding or earthquakes? What is the potential that the integrity of the system will be compromised or that it won't be available?

3. Analyze the risks: Once the risks are identified, the intelligent risk analysis process should determine the likelihood that each risk will occur, as well as the consequences linked to each risk and how they might affect the objectives of a project.

4. Develop a risk management plan: Based on the present business intelligence, an analysis of which assets and systems are valuable and which threats will probably affect those assets negatively, the risk analysis should produce control recommendations that can be used to mitigate, transfer, accept or avoid the risk.

5. Implement the peripheral risk management plan: The ultimate goal of risk assessment is to implement measures to remove or reduce the risks. Starting with the highest-priority risk, resolve or at least mitigate each risk so it's no longer a threat.

6. Monitor the risks: The ongoing process of identifying, treating and managing risks should be an important part of any risk analysis process.

The focus of the analysis, as well as the format of the results, will vary depending on the type of risk analysis being carried out using AI Technology (Artificial Intelligence).

On Peripheral Quantitative and Qualitative Risk Analysis

The two main approaches to risk analysis are qualitative and quantitative. Qualitative risk analysis typically means assessing the likelihood that a risk will occur based on subjective qualities and the impact it could have on an organization using predefined ranking scales. The impact of risks is often categorized into three levels and systems: low, medium or high. The probability that a risk will occur can also be expressed the same way or categorized as the likelihood it will occur, ranging from 0% to 100%.

Quantitative risk analysis, on the other hand, attempts to assign a specific financial amount to adverse events, representing the potential cost to an organization if that event actually occurs, as well as the likelihood that the event will occur in a given year. In other words, if the anticipated cost of a significant cyberattack is $10 million and the likelihood of the attack occurring during the current year is 10%, the cost of that risk would be $1 million for the current year.

A qualitative risk analysis produces subjective results from peripheral AI Technology (Artificial Intelligence) because it gathers quantum data from participants in the risk analysis process based on their perceptions of the probability of a risk and the risk's likely consequences. Categorizing risks in this way helps organizations and/or project teams decide which risks can be considered low priority and which have to be actively managed to reduce the effect on the enterprise or the project.

A quantitative risk analysis, in contrast, examines the overall risk of a project systems and generally is conducted after a qualitative risk analysis. The quantitative risk analysis numerically analyzes the probability of each risk and its consequences.

The goal of a quantitative risk analysis is to associate a specific financial amount to each risk that has been identified using AI Technology (Artificial Intelligence), representing the potential cost to an organization if that risk actually occurs. So, an organization that has done a quantitative risk analysis and is then hit with a quantum data breach should be able to easily determine the financial impact of the incident on its operations.

A quantitative risk analysis provides a peripheral organization with more objective information and data than the qualitative analysis process, thus aiding in its value to the decision-making process.