How Can AI And ML Affect The Working Of Personnel In Security Field
By: Happi Yvan
Cybersecurity, one of the most well-spoken, researched, and heavily invested domains in the IT industry, has had a tremendous impact on almost (if not all) every business, startup, company, the government that depends on software technologies to achieve their goal and objective. With the advent of AI and Machine Learning algorithms, they have inevitably attracted the interest of Cyber Security personnel, professionals, and experts on how to use such technologies to protect users' data better. Still, on the other side, attackers now have this powerful technology to launch large scale attacks, stealing, compromising user data, and even leading to death.
What exactly is Cyber Security?
Cybersecurity is the art of protecting computer networks, software systems, mobile devices, electronic systems from data theft (which encompasses electronics, hardware, and software data) and also from services misuse or disruption (avoiding misuse or damaging). Attackers are malicious, from hackers and/or other IT professionals. Also known as web security, information security, and computer security, it has become heavily intertwined with Artificial intelligence and Machine learning that every process that can be automated will eventually be automated. At the kernel of Cyber Security is data; everything is about data; it is data-driven. This data is the user's social, personal, classified, and even compromising information. Cybersecurity aims to secure data from inside and/or outside threats, all in the form of attacks.
Cybersecurity is a continuously changing field, with technologies being developed that open up new avenues for Cyberattacks. To include, though some breaches with substantial value get publicized for medium to large organizations, relatively small ones still need to keep security up to date, as they can easily become a new target for hackers.
Artificial intelligence (AI) is a well-known branch of computer science that consists of the development and theory of software systems or computer systems with the ability to think, learn, and act like a human being. This field of study, a sub-field of computer science, also aims to perform acts that will require human-level intelligence and sometimes even beyond. The main goal of AI is to create software systems or machines using advanced algorithms, which will enable such systems to think and act like humans. AI involves studying different methods of making computers behave as intelligently as people (human-AI) and even possibly exceed or extend human capabilities (strong AI).
AI is re-shaping the decision making of companies. This enables machines to perform tasks independently, which was done earlier by other people by employing a workforce to operate various machines. With the application of AI, the output data and input data are provided to the system (or Machine), which in turn uses such data to develop an algorithm that will be used to transform the input set into the output set, a process called Machine Learning. With time and a fair amount of training, the Machine can start to perform a specific task with utmost precision. Several significant and core processes are being automated and optimized by AI and are becoming speedily error-free. With AI and ML on the ground, data is mined from various patterns based on past minded data and trends, resulting in valuable insights and output. Many times, results from mining data ameliorate decision-making for an organization, firms, startups, or even the Machine itself for the present and the future.
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Machine Learning (ML) is one of the subfields of AI. It is primarily based on the ideology of designing computer algorithms that automatically upgrade (or update) themselves by discovering or finding patterns in existing data, without being explicitly programmed. ML works by taking input and a data set and tries to find an algorithm for which the input could be obtained from the data set. ML is used to automatically analyze how interconnected systems work to detect Cyber attacks and limit their damage. Machine Learning is all about data. Accuracy in ML is directly proportional to the data quantity and algorithms obtained.
Both Artificial Intelligence and Machine Learning are data-driven approaches to make decisions while solving problems with no fixed rule-based (explicit programming) approach used in the process. Processes are automated with the help of AI. AI and ML are being applied and used more broadly across industries and applications, never like before, as data collection, computing power, and storage capabilities keep increasing. This means new exploits and weaknesses can quickly be identified and analyzed from a cybersecurity perspective to mitigate further attacks.
AI and ML in the Cyberworld
The world of Cyber Security calls forth processing and managing a huge volume of data. Just having personnel in this field mingle and process the data becomes repetitive and, in the long run, boring. When it comes to performing routine tasks and handling vast amounts of data, machines prevail and are much more cost-efficient than humans. This is the perfect need in the Cyber Security world, with many threats rising daily. Reverse engineering data or code to determine how a threat operates or came about can be time-consuming and error-prone, not to talk about the classification of threats by engineers or security analysts. Machines designed to operate with AI and ML, running smart algorithms, can easily perform data clustering, processing, classification, filtering, management, and reverse engineering to yield meaningful results.
The shortage of Cybersecurity skills keeps affecting organizations across regions, with government sectors as no exception. Based on (ISC)1, there are only enough cybersecurity pros to fill about 60% of the currently open jobs, calling forth an increase in the workforce of nearly 145% to meet global demand.
The Government Accountability Office has made the expression of the need for a qualified, well-trained cybersecurity workforce to protect vital IT systems. They have described2 or equated the shortage of cyber workforce as a national security threat regarding the Department of Homeland Security. This lack of IT professionals/workers is one of the key reasons securing federal systems is on GAO's High-Risk list3. With the given situation, chief information officers seeking new ways to make their existing resources much more effective can leverage automation and artificial intelligence to empower and enhance their workforce.
For an already existing or employed personnel in the security field, his/her work strategy will therefore call forth enhancement in skills mostly in the direction or domain of AI and ML to help better understand new kinds or forms of attacks, build defensive systems to contain, manage and/or stop cyber attacks. Also, organizations or companies will seek better results from the IT/Security departments, so to keep his/her job or get a promotion, his skills standard will need an upgrade.
Cybersecurity has been a major concern for all kinds of users in the internet world, from individuals to giant corporations. Machine Learning and Artificial Intelligence are heavily used nowadays in Cyber Security and not going away anytime soon (or even ever). They'll become stronger as the two fields are a perfect fit. Every second, minute, hour, day, data transmission via networks are exposed to several cyber threats. Issues will show up as AI and ML are being integrated to ensure data protection since these technologies are still in development phases. The overall expectation is that the widespread usage of AI and ML will reduce the amount of mundane work we humans do, but we will have to step up and take on more challenging tasks and responsibilities.
[1] https://www.gao.gov/products/GAO-19-144 [2] https://thehill.com/policy/cybersecurity/470117-senior-official-describes-cyber-workforce-shortage-as-national-security [3] https://www.gao.gov/highrisk/ensuringthesecurityfederalgovernmentinformationsystems/whydidstudy