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Sick And Tired Of Doing Artificial Intelligence The Old Way? Read This

What is Artificial Intelligence? 

Artificial intelligence (AI) is a broad field of computer science that focuses on creating intelligent machines that can execute functions that would otherwise require human intelligence. AI is a multidisciplinary discipline with many methods, but advances in machine learning and deep learning are causing a paradigm change in almost every industry.

AI is a field of computer science that seeks to mimic or emulate human intelligence in computers at the most basic level. Artificial intelligence’s large objective has ignited a slew of questions and debates. So much so that there is no widely agreed description of the field.

HOW DOES ARTIFICIAL INTELLIGENCE WORK?

Artificial intelligence can be classified into two categories:

AI with a limited scope: This kind of artificial intelligence, also known as “weak AI,” works in a restricted sense and is a simulation of human intelligence. Although narrow AI is always based on executing a single task exceedingly well, these devices work under much more restrictions and limits than even the most simple human intellect. Artificial General Intelligence (AGI): AGI, also known as “Strong AI,” is the kind of artificial intelligence that we see in movies like Westworld’s robots or Star Trek: The Next Generation’s Data. AGI is a computer that has general intelligence and can use that intelligence to solve any problem, just as a human can.

HISTORY OF ARTIFICIAL INTELLIGENCE

Ancient Greek mythology included intelligent robots and artificial beings for the first time. The development of syllogism and its introduction to deductive logic by Aristotle was a watershed moment in humanity’s attempt to comprehend its own intellect. Despite its long and deep origins, artificial intelligence as we know it today has only been around for a century.

Basics in Artificial Intelligence

Artificial intelligence (AI) refers to systems that can comprehend, read, and function in obtained and generated data. AI today operates in three ways:

Assisted data, which is already freely accessible, enhances what individuals and organisations are already doing.

People and organisations will now do something they couldn’t do before thanks to augmented reality, which is just getting started.

Autonomous intelligence is a form of artificial intelligence that is being designed for the future. It consists of computers that operate independently. Self-driving cars, as they become widely used, would be an example of this.

AI may be said to have certain elements of human intelligence, such as a store of domain-specific knowledge, mechanisms for acquiring new information, and mechanisms for bringing the information to use.

Today’s AI technology includes machine intelligence, expert algorithms, neural networks, and deep learning, to name a few instances or subsets.

Machine learning employs mathematical methods to allow computers to “learn” (e.g., boost output over time) from data rather than being directly programmed. Machine learning performs well when it is focused on a single goal rather than a broad mission.

Expert systems are computer programmes that address problems in specific domains. They solve problems and make decisions using fuzzy rules-based logic and closely selected bodies of information, mimicking the thinking of human experts.

Neural networks are a programming model inspired by biology that allows a machine to learn from observational data. Each node in a neural network assigns a weight to its data, showing how right or incorrect it is in relation to the process at hand. The sum of these weights is then used to calculate the final product.

Deep learning is a form of machine learning that is focused on learning data representations rather than task-specific algorithms. Deep learning-based image processing is now often superior to humans in a range of fields, including autonomous vehicles, scan analyses, and medical diagnosis.

Applying artificial intelligence to cybersecurity

AI is well-suited to solving some of the world’s most challenging challenges, and cybersecurity is surely one of them. Machine learning and AI will be used to “keep up with the bad guys” in today’s ever-evolving cyber-attacks and the explosion of smartphones, automating vulnerability identification and responding more effectively than conventional software-driven approaches. Cybersecurity, on the other hand, poses several special challenges:

A wide assault field.

Thousands or tens of thousands of computers per company

There are hundreds of attack vectors to choose from.

There are significant shortages of trained security personnel.

Massive amounts of data that have developed beyond the reach of a human issue

Many of these issues should be solved by a self-learning, AI-based cybersecurity posture management system. There are technologies available to better train a self-learning machine to collect data from around the business information systems in a continuous and autonomous manner.

Following that, the data is processed and used to conduct pattern correlation across millions to billions of signals specific to the enterprise attack surface. As a result, new levels of intelligence are being fed to human teams in a variety of cybersecurity categories, including:

IT Asset Inventory – compiling a full and comprehensive list of all computers, customers, and programmes with links to information systems. In inventory, categorization and calculation of market criticality are also important.

Threat Exposure – Hackers, like anyone else, track patterns, so what’s trendy for hackers shifts on a daily basis. AI-driven cybersecurity tools can provide up-to-date awareness of global and industry-specific risks, allowing you to prioritise threats based not just on what might be used to target your company, but rather on what is most likely to be used to attack your company.

Controls Effectiveness – To sustain a strong security strategy, it’s critical to consider the effects of the different security tools and processes you’ve implemented. AI will help you find out where the information security software excels and where it falls short.

AI-based programmes can forecast if and when you are most likely to be compromised, taking into account IT asset inventory, vulnerability presence, and controls effectiveness, so you can allocate resources and tools to places of vulnerability. Prescriptive knowledge obtained from AI research will assist you in configuring and optimising controls and processes to produce the best performance.

Incident response – AI-powered applications may have a better background for prioritising and responding to vulnerability threats, for fast incident response, and for surfacing root causes in order to eliminate bugs and prevent potential problems.

Explainability of recommendations and review is key to using AI to complement human information security teams. This is crucial for achieving buy-in from stakeholders around the company, recognising the effect of various information management initiatives, and reporting relevant data to all stakeholders, including end customers, security operations, the CISO, auditors, the CIO, CEO, and the board of directors.

Adversaries’ Use of AI

Instead of actively running after malicious behaviour, IT security practitioners will use AI and machine learning (ML) to implement sound cybersecurity policies and shrink the threat surface. State-sponsored criminals, terrorist cyber-gangs, and ideological hackers, on the other hand, may use the same AI tactics to bypass protections and evade detection.

The “AI/cybersecurity conundrum” exists here. Companies will need to be aware of the possible drawbacks of AI as it matures and expands into the cybersecurity space:

Hackers can defeat security algorithms by targeting the data they train on and the warning flags they search for, so machine learning and artificial intelligence can help protect against cyber-attacks.

Hackers may also use AI to circumvent protections and build mutating malware that alters its configuration in order to prevent detection.

AI systems can provide misleading findings and false negatives if they are not fed large amounts of data and incidents.

Organizations would fail to retrieve the right data that feeds their AI programmes if data theft goes undetected, with potentially catastrophic results.

Conclusion 

AI has emerged as a necessary technology for augmenting the contributions of human information management teams in recent years. Since humans can no longer defend the complex organisational attack surface effectively, AI offers much-needed research and vulnerability detection that can be used by cybersecurity experts to reduce intrusion risk and enhance protection posture. In the field of security, AI can recognise and prioritise danger, detect malware on a network instantly, guide incident response, and detect intrusions before they occur.

AI enables cybersecurity teams to form powerful human-machine collaborations that extend our expertise, enhance our lives, and propel cybersecurity in ways that seem to be greater than the number of their parts.

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