01/08/2024
https://www.spetsnazsecurityinternational.co.uk/Services-Worldwide-Close-Personal-Protection-Service-Guards
Investigation of innovation of Spetsnaz Security International Fidel Matola for a rapidly changing environment
Name: Fidel Matola Spetsnaz Security International Fidel Matola
Institution name: University
Submission date- 16/6/2024
Contact details:
Email: [email protected]
Website: https://www.spetsnazsecurityinternational.co.uk/Services-Worldwide-Close-Personal-Protection-Service-Guards
Mobile: Mobile: (+44) 0759 957 4524 (Viber & WhatsApp)
Signed statement
I, Fidel Matola, hereby declare that this dissertation, entitled "Investigation of Innovation of Spetsnaz Security International Fidel Matola for a rapidly changing environment." is entirely my own work and has not been submitted for any other degree or professional qualification at this or any other institution.
Signature: ………………Fidel Matola Spetsnaz Security International.………………….
Acknowledgments
I would like to express my deepest gratitude to everyone, for their invaluable guidance, support, and encouragement throughout the course of this dissertation. Their expertise and insightful feedback have been crucial in shaping this research project, and I am truly grateful for their time and dedication.
My appreciation extends to the faculty and staff of the University for their support and the conducive learning environment they have created.
I would like to acknowledge the unwavering support and encouragement of my family and friends who have been a constant source of inspiration and motivation during my academic journey.
Finally, I dedicate this dissertation to ....
Fidel Matola
Abstract:
Based on the innovations in technology this research will provide" How the integration of AI can enhance the threat detection abilities of Spetsnaz Security International Fidel Matola International Limited and improve its security operations". AI is one of the main technology innovations that can be used for the defence of internet-related systems like attacks, damages, cyber threats, or non-authorised access. To mindfully solve cyber security solves cyber security issues, AI techniques that include machine learning, and the idea of natural language can be utilised. The research aims of this study have been divided into how the company is incorporating artificial intelligence, ideas for improving the integration of AI in the company, placement of AI-driven anomaly detection techniques, and how it will affect increasing security operations and threat detection capabilities. In regards to this research, the primary data collection method such as a survey questionnaire is selected. The justification for choosing this method of data collection is that it helps in offering unbiased and relatable data highlighting the implementation of AI along with its threat detection and security issues, particularly in Spetsnaz Security International Fidel Matola International. The findings of the research showed that AI integration tends to have a positive influence on the skills of the company to detect threats timely and enhance overall security functioning. The research also states that there are some approaches such as Traditional approaches which must be replaced with advanced methods to further enhance security operations. Recommendations from the study findings are Spetsnaz Security International Fidel Matola should adopt artificial intelligence-based cyber security schemes to give enhanced preciseness and effectiveness as contrasted to usual security solutions. Also, technology innovation can help them respond faster to attacks by automating particular tasks, such as redirecting traffic away from a susceptible server or changing their IT team to possible difficulties.
Contents
Investigation of innovation of Spetsnaz Security International Fidel Matola for a rapidly changing environment 1
Signed statement 2
I, Spetsnaz Security International Fidel Matola hereby state that this paper "Investigation of innovation of Spetsnaz Security International Fidel Matola for a rapidly changing environment", is completely my own work and has not been defer to for any other degree or professional condition at this or any other foundation. 2
Acknowledgments 3
Abstract: 4
Chapter 1: Introduction: 8
The topic of introduction and its significance: 8
Research question: 9
Objectives of the study: 10
An overview of the dissertation structure: 10
Chapter 1: Introduction: 10
Chapter 2: Literature Review: 10
Chapter 3: Methodology: 10
Chapter 4: Findings: 10
Chapter 5: Discussion: 11
Chapter 6: Conclusion: 11
Chapter 2: Literature Review 12
AI Integration in Security Organizations: 12
Application of AI in threat detection: 13
Anomaly Detection Techniques in AI: 16
Challenges and Considerations in AI Integration: 18
Impact of AI on Security Operations: 20
Chapter 3 Methodology 22
Research Philosophy 22
Positivism 22
Interpretivism 23
Ontology 24
Epistemology 24
Research Approach 25
Research Strategy 25
Methods of Data Collection 27
Research Sampling 28
Data Analysis 29
Research Limitations 30
Ethical Considerations 30
Chapter 4 Findings and Analysis 31
Findings 32
Artificial Intelligence (AI) helps organisations in reducing incident response time, financial losses and practise the best security strategies 32
Security companies may effectively establish AI in the company by associating with the professionals of Artificial Intelligence 33
Contact details:
Email: [email protected]
Website: https://www.spetsnazsecurityinternational.co.uk/Services-Worldwide-Close-Personal-Protection-Service-Guards
Mobile: Mobile: (+44) 0759 957 4524 (Viber & WhatsApp)
The development of thorough strategic plans is an ideal approach for AI integration in the cybersecurity systems of organisations 34
Organisations use Traditional security techniques incorporating signatures and indicators to recognise the threats 35
Signature-based techniques also help in detecting threats related to AI 36
Chapter 5 Discussion 41
Chapter 6 Conclusion and Recommendations 44
References 46
Appendix 52
Survey Questionnaire 52
Chapter 1: Introduction:
The topic of introduction and its significance:
The modern world is characterised by rapid technological enhancement and an ever-changing landscape of security concerns. In this era, security has become an identically main concern for individuals, companies, and nations. Spetsnaz Security International Fidel Matola International Limited is a leading security organisation in the UK that has been at the forefront of giving high-end protection and risk administration services to customers worldwide (Tan et al., 2022). However as the landscape of security continues to alter, the need for enhanced theft detection and prevention mechanisms has grown exponentially. Commonly used security measures are no longer sufficient to combat the enhancing sophisticated threats that are been posed by criminals and attackers.
To address these hurdles efficiently, the involvement of Artificial Intelligence (AI) technologies has become imperative. AI provides a new frontier in security functions, giving intelligent video analytics and anomaly detection abilities that can change the way security is sustained and threats are detected. By the analysis of information, patterns and predictive modelling AI gives powers to companies like Spetsnaz Security International Fidel Matola International to react and respond to any possible threats proactively. This research delves into the involvement of AI in the functions of Spetsnaz Security International Fidel Matola International and examines its possible effects on increasing threat detection capabilities and improving overall security operations (Tan et al., 2022).
The consequence of this study goes beyond the scope of academic examination, as its effects are poised to enhance far and wide, echoing throughout the security industry and beyond. At its main, this research is poised to provide invaluable insights that hold the strength to restructure the security of Spetsnaz Security International Fidel Matola International. In the times when AI technologies continue their relentless march of progress, the capability to comprehensively understand how these enhancements can be strategically and efficiently harnessed within the realm of security functions has a main significance. This study is not merely an intellectual exercise but a practical roadmap, illuminating the path forward for security companies, policymakers and stakeholders alike (Mallikarjunaradhya, Pothukuchi and Kota, 2023). For security companies the findings of this research represent a blueprint for communicating the intricate landscape of AI integration, providing concrete strategies and best practices for deploying AI AI-directed solutions to increase their operational abilities. By leveraging the insights gained from this study, security companies can not only bolster their capability to safeguard their customers' assets but also adapt to the ever-changing threat landscape with agility and precision.
Policymakers are presented with special chances to craft informed and forward-thinking regulations and guidelines surrounding AI integration within the security sector. As AI technologies become progressively significant to national and global security sectors. As AI technologies have become gradually important to national and worldwide security strategies, policymakers can draw upon the research's findings to ensure the accountable and ethical enhancement of AI in the facility of safeguarding nations and their interests. The broader importance of this research goes beyond the realm of security alone. It underscores the main role that AI integration plays across different industries, shedding light on the possible implications and hurdles that come with the adoption of AI technologies (Mallikarjunaradhya, Pothukuchi and Kota, 2023).
Research question:
How the integration of AI can enhance the threat detection abilities of Spetsnaz Security International Fidel Matola International Limited and improve its security operations?
Objectives of the study:
This research aims to examine the integration of Artificial Intelligence (AI) in Spetsnaz Security International Fidel Matola International and assess its impact on enhancing threat detection capabilities and improving security operations
● Examine how the company is incorporating artificial intelligence.
● Provide ideas for improving the integration of AI in the company
● Evaluate the placement of AI-driven anomaly detection techniques.
● Conclude how it will affect increasing security operations and threat detection capabilities.
An overview of the dissertation structure:
Chapter 1: Introduction:
This chapter starts with the introduction to the research topic, and then its significance will be discussed. After this, there will be an explanation of the objectives of the study (Truong, Diep and Zelinka, 2020).
Chapter 2: Literature Review:
In this chapter, there will be a discussion of literature, theories, and empirical studies that are relevant to the study. Then there will be recognition of any gap in the literature.
Chapter 3: Methodology:
In this chapter, there will be a discussion of the research design, justification of the chosen design, and discussion of the data gathering method and sampling techniques.
Chapter 4: Findings:
In this chapter, there will be the presentation of findings from data in an organised way.
Chapter 5: Discussion:
In this chapter, there will be an interpretation of the findings and their relevance concerning research questions.
Chapter 6: Conclusion:
In this chapter there will be a summary of all the main findings, and then recommendations based on those findings, the suggestions for future research (Truong, Diep and Zelinka, 2020).
Chapter 2: Literature Review
AI Integration in Security Organizations:
With technological advancement threat to cyber security is also increasing, and this has increased the need to incorporate artificial intelligence (AI) into security organisations. Organisations use different methods to integrate AI into their cyber practice.
As defined in work done by Anderson (2020), AI can also be used in cyber security, according to the report by Norton, highlighting that the cost for data breach recovery is $3.86 million globally. This resort has also highlighted that companies usually need 196 days to recover from this data breach. AI integration helps companies avoid this time and financial losses. AI, machine learning, and threat intelligence are designed to identify patterns in security systems according to past experiences. AI and machine learning also help organisations in reducing incident response time and practise the best security strategies.
To successfully integrate AI-based techniques organisation has to assess their current Cyber security level evaluates their effectiveness and identify their weaknesses. This strategy is more likely to assist organisations in integrating AI cyber security where needed (Roohparvar, 2023).
According to the needs of cyber security companies have to select the right AI technology. Several AI technologies can be used to improve cyber security. These technologies include machine learning, deep learning, and natural language processing (Roohparvar, 2023).
According to Abdulaziz Aldoseri, Al-Khalifa, and Abdel Magid Hamouda (2023), The development of thorough strategic plans is an ideal approach for AI integration in the cybersecurity systems of organisations. These plans are likely to offer a roadmap for ex*****on while redefining and aligning these plans with organisation's AI goals and investment techniques. These plans would ensure a more efficient integration process by coordinating AI projects with the security goals of organisations. The frequent allocation of sizable funds for the acquisition of new AI technologies is also an indication of the organisation's commitment to addressing potential issues or security risks.
As suggested by Haleem et al. (2022), Security companies can work with AI professionals whether from their industries or others. These partnerships accommodate easy information sharing enabling security professionals to benefit from AI experts' experience and practise effective AI integration in cyber security.
Organisations often use Traditional security techniques incorporating signatures and indicators to recognise the threats which may work with the past faced threats but they can not be effective for the unpredictable threats. Signature-based techniques are found to detect approximately 90% of threats. Integration of AI can increase this rate to 95% but with the risk of false positives. The recommended strategy to increase this rate to 100% is to combine the signature-based technique and AI which ultimately increases the detection rate (Anderson, 2020).
AI would monitor the essential data centre processes including backup power, cooling filters, power consumption, internal temperatures, and bandwidth usage. AI has the ability to calculate and continuously monitor the values that enhance the effectiveness and security of hardware and infrastructure. Integration of AI reduces hardware maintenance by alerting when needed (Anderson, 2020).
Application of AI in threat detection:
As per the study of Ansari et al. (2022), a new era of security measures has begun with the threat detection integration of Artificial Intelligence (AI), showing remarkable applications across both public and private sectors. AI-driven technologies, in particular machine learning algorithms, have revolutionised the detection and mitigation of cyber threats in the field of cyber security. Intrusion detection systems built on machine learning are at the frontline of this revolution, regularly examining through enormous datasets to allow the proactive detection of abnormal patterns and potential attacks. With the help of cyber security companies, organisations can now effectively protect their digital assets thanks to the power of AI-driven anomaly detection. Additionally, AI-powered email security solutions, such as those offered by many organisations, employ cutting-edge pattern recognition and natural language processing techniques to detect phishing attempts with weird accuracy (Ansari et al., 2022).
According to Bécue, Praça and Gama (2021), AI has made significant advancements in physical security, particularly in the areas of video surveillance and facial recognition. Initiatives in the public sector have adopted AI-driven face recognition as a powerful tool to identify suspects in busy public areas, considerably increasing law enforcement efforts. Retail stores and other private organisations have benefited from AI-driven video analytics to spot and anticipate suspicious behaviour, which has reduced theft instances and improved overall customer safety. The importance of AI-driven threat detection extends to critical infrastructure as well, as AI-powered intrusion detection systems combine a range of data inputs, such as sensors and cameras, with machine learning algorithms. This integrated method makes it possible to quickly identify unauthorised access, providing a chance for preventative security steps (Bécue, Praça and Gama, 2021).
The applications of AI in threat detection in the public sector include airport security, where AI scanners have proven crucial in spotting hidden hazards and assuring passenger safety. As per the study of Dash et al. (2022), these AI-powered scanners not only improve security procedures but also speed up passenger screening procedures, providing a comprehensive illustration of AI's successful application in the public sector. The influence of AI also extends to the early detection of disease outbreaks, a significant public health issue. The early detection of potential disease outbreaks is made possible by the adoption of AI models by public health organisations all around the world. These models analyse data from various sources, including social media platforms and medical records. During the COVID-19 pandemic, when AI was important in tracking and controlling the virus' transmission, this skill was very clear (Dash et al., 2022).
Contact details:
Email: [email protected]
Website: https://www.spetsnazsecurityinternational.co.uk/Services-Worldwide-Close-Personal-Protection-Service-Guards
Mobile: Mobile: (+44) 0759 957 4524 (Viber & WhatsApp)
According to the research of Kunduru (2023), using AI for threat detection is a dynamic, diverse field with significant consequences for security across a range of industries. It not only improves our ability to recognise threats and take action against them but also optimises security procedures. The significance of addressing the ethical and privacy issues that come with this technological breakthrough must be emphasised, though. As AI develops further, its role in threat detection will grow, providing a proactive and adaptive strategy to protect against new threats and vulnerabilities in a constantly changing world. Realising AI-driven threat detection's full potential in upholding a safe and secure environment for people, businesses, and nations will depend on its careful and efficient implementation (Kunduru, 2023).
According to the study by Lee et al. (2019), an important paradigm shift in how security is seen in both the public and private sectors will result from the use of artificial intelligence (AI) in threat detection. As we go more into the field of cyber security Lee et al. (2019), find that AI-driven solutions have not only revolutionised threat detection but also greatly improved the capacity to respond to new cyber threats. Organisations can proactively discover abnormal trends and foresee future interruptions because of their ongoing analysis of huge and complicated information. This defensive approach has shown to be quite useful at a time when cyber threats are becoming more complex and common (Lee et al., 2019).
Anomaly Detection Techniques in AI:
In cyber security anomaly detection is an essential part of operations as it observes the odd patterns and unidentified or behaviours highlighting potential threats or weaknesses. this has improved significantly with the integration of Artificial intelligence (AI) in recent years across different sectors, including cybersecurity, fraud detection, and industrial monitoring (Alla and Adari, 2019).
This process is about data mining involves determining outlier values in a set or series of data. it works by assuming that the given data belongs to the specific and understood range which can based on past data and the values outside this range are found in rare cases (Alla and Adari, 2019).
As cyber threats are evolving day by day it is difficult for Traditional rule-based systems have a hard time adjust to new types of attacks AI has become a crucial strategy to advance anomaly detection techniques (Karimipour and Derakhshan, 2021).
Machine learning and deep learning techniques drive AI-driven anomaly detection. Supervised learning techniques like Support Vector Machines (SVM) and Random Forests spot the abnormalities thoroughly when labelled data is available for training as they are a group of supervised learning methods designed for classification, regression and outliers detection. but these methods may not be sufficient as they face trouble with datasets that are unbalanced and changing threat environments (Güngör and Hancke, 2017).
Unsupervised learning techniques are also excellent at spotting abnormalities without relying on labelled data, such as density-based approaches like isolation forests and clustering algorithms like k-means. They might be more efficient for spotting new dangers along with capabilities to adjust to shifting trends. in sequence- and image-based anomaly detection applications Deep learning has shown impressive performance, specifically while using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) (Aggarwal, 2016).
Feature engineering is essential For AI-driven anomaly detection to be successful it is about selecting, manipulating and transforming raw data into the elements that are usable in supervised learning. Two examples of feature selection or dimensionality reduction approaches are Principal Component Analysis (PCA) and auto encoders assisting in separating important information from complex data while minimising noise. The precision and effectiveness of models for anomaly identification can be improved by these methods (Bandyopadhyay, Rout and Satapathy, 2021).
In security situations to quickly address risks Real-time anomaly detection is crucial. AI-driven models can be set up in streaming contexts to process data as it comes in continuously. This process is important in situations that change quickly as it opts for quick detection and action (Karimipour and Derakhshan, 2021).
Ensemble methods create several models and combine them to provide improved results, which increase resilience and lower false positives, enhancing anomaly identification. Transfer learning, speeds up model convergence and performance, especially when there is a dearth of labelled data as it involves optimising pre-trained models for anomaly detection tasks. Even though AI-driven anomaly detection has many benefits, there are still some issues to mitigate. These may involve processing high-dimensional data to make sure that data is secure (Karimipour and Derakhshan, 2021).
Challenges and Considerations in AI Integration:
The potential for enhancing threat detection, efficiency, and overall security posture through the integration of Artificial Intelligence (AI) into security organisations is undeniable. But it also has some challenges and barriers (Chaudhari, Kaur and Mrs Priyanka Gupta, 2020).
Privacy of data is one of the main concerns while considering the ethical issues in AI integration. Security organisations hold Sensitive data of their clients and integration of AI into the data may raise issues about privacy concerns. organisations have to employ strong data anonymization, encryption, and access control methods To protect sensitive information and align it to relevant legislation like GDPR or HIPAA (Chaudhari, Kaur and Mrs Priyanka Gupta, 2020).
Integration of AI into existing security infrastructures can be difficult and demanding. The successful integration may be obstructed by Compatibility problems, disparate data formats, and legacy systems. To align the AI integration with the current systems, organisations may carry out robust compatibility analyses, embrace open standards, and spend money on middleware solutions (Kaur, Tomar and Tanque, 2020).
In the context of security AI systems can be biassed, which can encourage prejudice and produce unjust results. This type of AI systems may misidentify risks or unfairly target particular groups. To achieve consistent outcomes, organisations often invest in diverse training datasets, use fairness-aware algorithms, and routinely audit their AI systems for prejudice (Kaur, Tomar and Tanque, 2020).
The lack of AI talent Is also an issue as it's a new field and organisations may struggle to find the best talents. AI experts can be difficult to find and may need extra resources to manage due to high demand in the labour market (Kaur, Tomar and Tanque, 2020).
Artificial intelligence (AI) systems are susceptible to adversarial attacks, in which nefarious individuals fudge inputs to trick AI models. It is crucial to ensure the reliability and security of AI systems. AI integration can be protected by performing regular vulnerability scans, using adversarial training methods, and applying intrusion detection systems in place (Stahl, 2021).
Adherence to laws and ethical norms while integrating AI is essential. Security organisations have to manage a challenging environment of constantly changing rules, based on the region or industry. To mitigate risks related to regulations they have to constantly update their practices with the help of legal professionals, and follow complete compliance procedures into practice (Stahl, 2021).
Some AI models' like "black-box" structure produces concerns about their transparency and comprehensibility. It is essential to comprehend ways of AI systems while making judgments, especially in security issues where Important assets or human lives can be at risk. Businesses should increase their investment on interpreting AI methods and creating open decision-making procedures (Stahl, 2021).
Achieving a profitable return on investment is a reasonable worry because implementing AI technologies can be expensive. To make justified investment decisions in AI integration they have to make sure that they are in line with strategic security objectives, cost-benefit evaluation while considering both short-term advantages and long-term benefits (Stahl, 2021).
Impact of AI on Security Operations:
Due to the integration of artificial intelligence a new way of threat identification and mitigation has started making The operations of security organisations smooth. Ai integration runs thread detection systems which are proven to be fairly effective in beginning unidentified dangers or security threats. Many studies have shown how AI algorithms can analyse massive volumes of data to find minute irregularities and trends that could indicate security breaches instantly (Montasari and Jahankhani, 2021).
The procedure to Increase the response accuracy has greatly helped by AI. It also assists security teams in responding more quickly and effectively to security alerts by automating regular processes like triaging alarms and gathering data. AI-driven incident response can shorten the average time it takes to determine and mitigate issues, potentially reducing harm (Montasari and Jahankhani, 2021).
False positives are also a problem And can divide the attention of analysts potentially leading to less attention towards real threats. To effectively lower false positives Machine learning models in particular have shown. Findings from research have shown how AI can improve algorithms over time, reducing false alerts and freeing up security professionals to concentrate on high-priority issues (Montasari and Jahankhani, 2021).
As per Ansari et al. (2022), artificial intelligence is opening up new opportunities for value creation in companies, societies, and the globe as a whole. It is observed that technology is very relevant in many cases of the world. Because of this many companies have included aspects of AI in different businesses and companies. Cyber security is an enhancing concept in the technology businesses. Several organisations have included information technology in their businesses. This factor has caused the companies to require more security measures. The effort to secure the attainable data and information has outcome in the enhancement of cyber security, and AI has been seen to impact cyber security mostly on a large scale. This factor has made machine learning to be important and included in the recent technologies backing cyber security. Different artificial intelligence technologies have all gotten dissimilar roles in making sure the cyber security operations. Research has been done on technology's role in security operations to ensure maximum efficiency in avoiding any attacks. Other organisations all around the world have the data that they need to be confidential. The technology has to make sure that no one can get access to this information. In future, there has also been an involvement of artificial intelligence on a main scale. These factors will ensure that artificial intelligence will be extremely enhanced to safeguard supreme security in organisations (Ansari et al., 2022).
According to Saeed et al. (2023), having a system that may secure themselves and detect any attempts is one of the aims of several organisations. The factor of safety is a dream that scholars and IT corporations are been aggressive to attain. The main key characteristic of artificial intelligence being used in systems is getting knowledge from their expertise. This is one of the necessary features of AI in general. It has been proven that systems may learn from the dissimilar feature that has prepared the technology mainly relevant in cyber security. AI has been thought of as the rescuer technology in cyber security. Education from expertise is an attribute of AI algorithms where systems can acquire from the aspects that have been done before. The procedures have been utilised in cyber security technologies and algorithms to make sure that an error cannot occur again. Outbreaks are been surrounded in systems where the AI algorithms will detect and understand the attack. In records security calculations and privacy, artificial technology has been one of the main technologies in making sure that they have enhanced those (Saeed et al., 2023). Data is necessary for business so they need secure options, with the help of artificial intelligence the system can help with great encryption and secure the privacy of the records included. Including AI learning systems cyber security assists in preventing spasms in a structure. The learning-based system studies the assailants' actions and alters to secure the data. This issue marks it unlikely for attackers to get access to the information (Saeed et al., 2023).
Chapter 3 Methodology
The methodology of the research helps in designing the research procedure as well as creating tools that are needed to carry out the research project. In addition, to make effective use of the tools, researchers are required to present and justify the tools in accordance with the research objectives. However, research projects may either use qualitative or quantitative data, while there is also an option of mixed research methods which involves both qualitative and quantitative information to conduct the research. In regards to this research quantitative approach is chosen.
Research Philosophy
In order to conduct the research project, it is required to have awareness regarding the research philosophy. Generally, there are some research philosophies that may be utilised such as positivism, interpretivism, ontology, and epistemology (Tamminen & Poucher, 2020). The chosen philosophy for this research is positivism as it best suits to highlight and supports the quantitative data collection.
Positivism
This research philosophy has faith in making use of the scientific, objective as well and systematic approach to the social world. In addition, this research philosophy consumes three primary factors which as the researcher's experience, logic as well and authenticity of the data which outlines the significance of the research. In regards to this approach, the decision-making is done on the basis of logic and related experiences (Alamgeer, 2023). The rationale for choosing this research philosophy is that it best fits with quantitative research to comprehend the link among the variables. This philosophy allows the research to test theory instead of creating it. More so, this philosophy is dependent on the quantitative data which can be gathered by utilising different tools, for instance, experimentation or structured surveys. This is the right opposite of the interpretivism (Junjie & Yingxin, 2022). Furthermore, positivism is mainly utilised in quantitative research while making use of the deductive reasoning method for testing the hypothesis. As well as it is also mainly dependent on the data-driven method in which researchers need to separate themselves from the context, environmental settings, and populations. Therefore, using a positivist research philosophy is required to remain independent and objective. In order to investigate the implementation of AI (Artificial Intelligence) in Spetsnaz Security International Fidel Matola International as well as to examine its influence on improving threat detection skills as well as to improve security functions, a statistical approach is used (Tamminen & Poucher, 2020).
Interpretivism
In contrast, interpretivism makes use of the inductive reasoning method for qualitative research. This type of research philosophy is subjective in nature. In addition, interpretivism allows the researchers to implement the elements of the research, therefore this research philosophy integrates the experiences of the humans in the research. Researchers using this type of research philosophy suppose that access to reality may only be done through social construction, for instance, shared meanings, language, and others. Whereas, the creation of the interpretivism philosophy is grounded on the analysis of positivism (Dudovskiy, 2022). In accordance with this, this type of research philosophy pays attention to qualitative analysis as compared to quantitative analysis. This is the reason, due to which this research philosophy is not chosen as it mainly prefers qualitative analysis, however, current research involves quantitative data. However, this type of research philosophy is connected with the logical place of idealism as well as is used to associate the varied techniques, concerning social constructivism, hermeneutics, and phenomenology which rejects the objectivist idea that meaning exists in the world independently of awareness (Tamminen & Poucher, 2020). In accordance with this type of research philosophy, it is crucial for the researcher to acknowledge differences among the individuals. More so, this research philosophy mainly pays attention to meaning as well as may use several approaches with the aim of reflecting varied factors of concern (Alharahsheh & Pius, 2020).
Ontology
This type of research philosophy clearly outlines the ideas as well as systematically assures for existence. The social structure using this research philosophy may be developed by observing the social initiatives as well as social concerns that may be considered for social purposes (Al-Ababneh, 2020). In addition, this type of research philosophy is mainly the theory of objects and their relations with each other. More so, it is regarded as the science in which theories are created and entails the claims regarding the object such as its appearance, interaction, and others. However, the reason for not choosing this research philosophy is that it does not require the development of a new theory (Mbanaso et al., 2023).
Epistemology
Epistemology research philosophy mainly pays attention to basic knowledge as well as understanding as the major aspect for the learner. In addition, this approach utilises three major concerns which are judgement, logic, and knowledge. More so, it mainly deals with the aim as well as the nature of the knowledge (Abu-Alhaija, 2019). More so, epistemology is intensely associated with ontology where it outlines what is constructed in reality. It is considered the most effective manner of investigating the nature of the world. In this regard, the researcher has some suppositions related to the research, which results in impacting the epistemological ideas of the researcher (Khatri, 2020).
Research Approach
It is necessary to choose the best research approach to carry out the research project. In general, there are two types of research approaches; inductive and deductive. In regards to this research, the deductive research approach is chosen which is associated with the scientific examinations while in contrast inductive approach is associated with the development of the particular hypothesis on the grounds of the literature review which is undertaken by the researcher in regards to the hypothesis development (Pandey, 2019).
More so, the deductive research approach starts functioning by observing the development of the theory. In regards to this research, the evidence is already collected which may be utilised for the development of the outcomes which are devoid of conclusions. The collected data assists in outlining the structure of the phenomena along with other themes for the development of the conceptual framework (Casula et al., 2020). In relation to the aim and objectives of this research, such as to investigate the implementation of AI in Spetsnaz Security International Fidel Matola as well as its influence on the improvement of threat detection skills, thus resulting in improving the security functions (Pandey, 2019). Therefore, the rationale for selecting the deductive research approach is that it supports finding out the causal link between the concepts as well as variables while supporting the assessment of the concepts quantitatively, meanwhile generalising the research outcomes. Additionally, another benefit of choosing this type of research approach is that it helps to examine the research problem from different perspectives (Young et al., 2019).
Research Strategy
The research strategies are regarded as the processes as well as other methods to attain or collect the data to accomplish targeted aims and objectives. In addition, this research also pays attention to the use of different methods, for instance, semi-experimental, correlation, meta-analysis, and other descriptive. However, selecting the most effective and suitable research strategy is associated with the creation of concepts related to the independent and dependent variables, methods of data gathering along other experimental designs to develop the statistical strategy (Hendren et al., 2022). More so, there are three kinds of research strategies, such as quantitative, mixed, and qualitative research strategies. However, the quantitative research design aims to discover the links among the independent as well as dependent variables and may be experimental or descriptive. Furthermore, the experiment intends to create links whereas the descriptive sections support creating the relationship between the variables. On the other hand, the experimental research strategy is regarded as an intervention as subjects are observed and intervened (Hall & Hall, 2020).
In addition, qualitative research strategy helps in carrying out the specific role of explaining life experiences (Dawadi et al., 2021). This type of research strategy helps to attain a view of the concept of the research. On the other hand, mixed strategy plays out role in finding out the concerns related to the research, by collecting data from different standpoints. More so, this type of research strategy is composed of both quantitative and qualitative data. In contrast, the quantitative research strategy allows the researcher to utilise inferential as well as descriptive statistics, on the contrary, qualitative research assists in providing details that are valuable in examining the objectives and aim of the research (Strijker et al., 2020).
In regards to the research project, the research strategy may either be quantitative or qualitative. While tool selection is done on the basis of the nature of the data. In regards, to this research, the chosen research strategy will outline the influence of the implementation of AI along with its associated threats and security issues, by means of quantitative data collection. In this regard, quantitative data analysis is used to carry out this type of research. The positivist philosophy also needs a quantitative approach. Additionally, the deductive approach also has an effective link with the quantitative analysis. On the other hand, qualitative data analysis involves views, perceptions, and information or non-numeric details which is not suitable to achieve the objectives of this research, due to which quantitative data analysis is chosen.
Methods of Data Collection
In general, there are two types of data collection methods such as primary and secondary data collection methods. The primary data collection method is considered as the method that helps in offering raw or first-hand data. Additionally, it also helps in offering in-depth knowledge of the research issue and also assists the researcher in making effective conclusions. More so, this type of data collection method is effective in providing authentic and reliable data which tends to produce the most effective research outcomes. However, this type of data collection method requires time and money (Lobe et al., 2020). While on the other hand, the secondary data collection method helps in interpreting as well as analysing the primary sources of data. This type of data collection method is less costly and takes less time as compared with the primary data collection method. In addition, this type of data can be collected from journal articles, previous research, information from the organisational website, and other newspapers. It is mainly the summary of the past research. However, this method of research sometimes fails to provide relatable data as well as sometimes offers biased information (Heap & Waters, 2019).
In regards to this research, the primary data collection method such as a survey questionnaire is selected. The foundation for selecting this method of data gathering is that it aids in offering unbiased and relatable data highlighting the implementation of AI along with its threat detection and security issues, particularly in Spetsnaz Security International Fidel Matola International. The survey questionnaire is used which helps in collecting data by means of close-ended questions (Islam & Islam, 2020). In addition, this type of primary data collection method helps the researcher to collect the information utilising different perspectives. However, there are a number of ways using which surveys may be carried out such as online, telephone, paper, and others. In regards to this research online survey questionnaire is chosen, as it is sent to the participants of the research on their official email address (Grassini & Laumann, 2020). There are several kinds of surveys such as it may either be qualitative or quantitative, while for examining the implementation of AI in the Spetsnaz Security International Fidel Matola international the quantitative survey questionnaire is carried out. additionally, options will be provided against each of the questions on the basis of the Likert-5 scale (Taherdoost, 2020). Therefore, this method of primary data collection method has allowed us to have greater control over the data mainly at the time of stating the research problems, which may be used effectively to attain the targeted goal and purposes of the research.
Research Sampling
Sampling is defined as the approach to examining the entire populace from which the data is collected on a small scale or on the sampling frame for providing the image of the whole populace. In addition, it is also crucial for the researcher to choose the most effective method of sampling. In general, there are a number of methods such as systematic sampling, stratified sampling, simple random sampling, and others (Bhardwaj, 2019). However, in regard to this research, the probability simple random sampling technique is used to ensure the non-manipulation of the research information. In addition, the probability method of sampling is regarded as the sampling technique that makes use of a random selection procedure (Zaman, 2019). In this regard, to make effective use of this type of sampling technique, a process is managed which assures that every member of the population has the same probability of being selected. As in the simple random sampling method, all the subsets of the populace have a fair and equivalent probability of getting designated. More so, this method of sampling also mitigates the chances of prejudice as well as simplifies the outcomes of the analysis (Iliyasu & Etikan, 2021). While on the other hand, this method of sampling may sometimes be vulnerable in case there is any mistake in the selection of the sample. However, in regards to the systematic sampling, the population is researched as well as structured in regards to maintaining the scheme and choosing components at daily intervals from the specified list. In regards to this research, the probability random sampling method is used which helps in choosing the most effective research participants from Spetsnaz Security International Fidel Matola International. The chosen sample size is 26 research participants composed of the employees working in the company.
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