Artificial intelligences (AIs) are becoming more and more integrated into our daily lives as well as the jobs we do. This means that AI security is becoming a bigger issue for more and more people. The most well-known security issue with AIs is the risk of an AI turning against its human users. This is sometimes referred to as an AI apocalypse, when the AI decides to kill off all human beings. However, this is only one of the many ways AIs can pose a security risk. AIs can also have human biases, which may lead to biased decisions in certain situations. AIs can also be hacked, and while the hacker may not be able to live with us in the real world, they can certainly use deep fake technologies to steal your data.
The Impact and Advantages of the AI Technology in Cybersecurity
In today’s scenario, none can deny the importance of artificial technology (AI). AI technology has been applied in many aspects, including few shot learning. With the advent of time, cybersecurity has also become a matter of discussion. In many cases, experts have raised concerns over cybersecurity for many reasons. Can AI resolve the cybersecurity issues? In the following section of this article, details on AI cybersecurity help will be discussed. Knowing the benefits of applying artificial intelligence to ensure better internet surfing safety is important for every technology enthusiast.
A Guide to AI Cybersecurity Help
Artificial Intelligence (AI) is an emerging technology. The technology can be used in different aspects. Many software or applications have been made with the integration of AI to deliver automated performances. Since cybersecurity has been a rising problem, many experts have tried to use artificial intelligence for achieving better security on the internet. According to many data scientists and experts, cybersecurity can get stronger with artificial intelligence and pre-training. Some of the benefits of AI cybersecurity help are discussed in the following section.
With artificial intelligence, automation is bound to happen. Artificial intelligence provides decision-making ability to the machines. It works on a large set of data to understand how a human reacts to different situations. Based on data, it makes decisions like humans. With automation, higher production can be achieved without human interventions.
AI can deal with cyber attacks without requiring human interventions. During cyber attacks, humans take time to recognize the problem and react accordingly. AI can recognize the problem instantly and apply the resistance to cyber-attacks.
2. Managing Vulnerability
Artificial intelligence helps in managing the vulnerability of cyberattacks. Presently, security solutions depend on the IT infrastructure for vulnerability management of various cyber-attacks. Hence, the present system takes time to judge the vulnerability and harmfulness of cyberattacks. On the other hand, AI-based systems are fast and efficient in understanding the vulnerability of a system.
It discovers the loopholes in a system for protecting the system from cyber-attacks. An AI-based cybersecurity management system can recognize the patterns and infiltration methods of various cyber-attacks. Apart from stopping the cyberattacks, it helps in recognizing patterns of different cyber attacks.
3. Authentication Improvement
If you want to know how can AI help cybersecurity, you should understand the vulnerability of the data authentication system. For authentication user data, many portals use login ID and password. Some portals have additionally added another layer of security. In this layer, you will receive an OTP on your mobile phone.
You have to enter the code received on your phone to move ahead. Such an authentication method is not fully secured. According to various studies, users keep passwords identical for different accounts.
Moreover, a large number of users do not match password strength too seriously. For authentication of the users on a web portal, stronger layers of security should be added. For example, portals should use biometric authentication, face recognition, and other technologies. All these advanced authentication methods heavily depend on artificial intelligence (AI) technology.
4. Behavioral Analysis with Artificial Intelligence
How can AI improve cybersecurity? Artificial Intelligence (AI) comes with behavioral analysis ability. Such an ability can provide better cybersecurity. AI keeps collecting data to understand the behaviors and preferences of a person.
How you access your system can be tracked and monitored closely by artificial intelligence. Hence, AI can create a working pattern of a user on a system or portal. If someone else steals your credentials and enters a system, AI can detect the danger through behavioral analysis. Thus, AI can take a self-motivated decision on blocking a user that can be a potential imposter.
5. AI for Controlling Phishing
Artificial Intelligence (AI) can control phishing, which is growing as a dangerous cybersecurity threat. Through phishing, the login credentials of users have been stolen. Such a technique is also used for introducing malware to the system.
For humans, it has become almost important to recognized phishing. But, artificial intelligence can easily recognize and neutralize such threats. AI can record all the common phishing sources and report them to the system quickly.
With early detection and reporting of the threats, it becomes easier for the system to deal with phishing. AI-based systems can also understand various phishing patterns according to geographic locations.
6. Hunting Various Threats
Artificial intelligence can easily detect threats. The ability of AI in detecting threats is stronger than human intelligence. Various systems develop AI threat detection shields that constantly search for possible threats on a system.
Whenever it detects a threat, it reports the problem to the system. On the other hand, the system takes the quickest measure to nullify the detected threat.
Future of AI in Cybersecurity
Experts have already realized and recognized the effectiveness of artificial intelligence (AI) in dealing with cyber threats. So, AI will become an important weapon for dealing with various cyber threats in the future. Some impacts of AI in dealing with cybersecurity are discussed in the following section of the article.
1. Countries Have Started Investing in AI for Cybersecurity
In the future, data will be the most important element for human civilization. The size of virtual data is rising, and threats to data security are becoming big concerns. In such a scenario, many countries have started investing in building infrastructure for maintaining top-class cybersecurity.
As per the sources, the USA and UK are the leading countries to invest in data security. Such data security infrastructure will largely depend on Artificial Intelligence (AI) technology.
2. Data Protection for Companies
Many people want to know how AI helps cybersecurity. Artificial Intelligence can fetch protection against cyber attacks in many ways. For example, it can recognize the threats through behavioral analysis.
The technology can efficiently detect the early threats and their possible solutions. Artificial Intelligence collects data on different kinds of cyber threats so that pattern of the cyber attackers can be identified. Hence, systems can prepare better strategies for nullifying cybersecurity threats.
3. Cloud Security and AI
Today, cloud data storage has become a common thing. People prefer cloud storage due to easy accessibility to data through multiple sources. Since the cloud has become a popular platform for data storage, cybersecurity attackers often target various cloud servers. In the last few years, the constant rise in cyber attacks on cloud storage has raised many concerns.
For dealing with such concerns, AI-based tools are used for data protection on the cloud. Using the AI tools will help you to integrate and manage business data on the cloud efficiently. Nevertheless, a business can avail better security for their data due to the presence of these AI tools.
4. Multi-Factor Authentication
In web and application authentication, usages of artificial intelligence technology can be noted quite commonly. For example, fingerprint and face detection has become common authentication techniques for smartphone devices and many other devices.
Many applications use fingerprints for the authentication of the users. With AI cybersecurity help, a multi-factor authentication system can be established for an application or web portal. Such an authentication system will ensure better cybersecurity.
5. Online Privacy for the Remote Users
Artificial intelligence will play a big role in maintaining online privacy for remote users. Today, the number of remote workers is increasing. After the Covid-19 pandemic, working from home has become a common thing. Instead of going to offices, people have to adopt the lifestyle of working from home. However, work from home has many challenges.
It becomes difficult to work with sensitive data from a home computer. Artificial Intelligence (AI) can enhance safety for remote workers. Companies can ask workers to work remotely with a more peaceful mind due to a strong AI cybersecurity system.
Some Drawbacks of AI for Cybersecurity
In the above section, you have learned how can AI be used in cybersecurity. It has many advantages, though there are some drawbacks too. Artificial Intelligence technology has been identified as the major force behind managing and neutralizing various cyber threats. But, there are a few challenges in implementing AI for cyber-security management. These challenges are discussed below.
1. AI Is Costly
Many countries have started building cyber-security shields that will be driven by artificial intelligence technology. However, building such systems is not easy. The main challenge is the cost. Many countries are not ready to adopt such advanced cybersecurity systems, as they cannot make such a huge investment in developing AI-based cybersecurity systems.
2. Unethical Use
Another big problem is the unethical use of artificial intelligence technology. Using AI can protect us from cyber attackers. At the same time, cyber attackers can gain massive power with artificial intelligence. They can also develop various cyber threats that will deploy AI technology. Dealing with such threats will become challenging.
Artificial intelligence brings automation. Hence, it increases the risk of unemployment. Manual cybersecurity monitoring will soon become obsolete. As a result, unemployment will become a prevailing problem.
Overall, it has to be concluded that AI-based systems will provide better security against cyber attackers. It can detect the threats early and neutralize them with suitable methods.
AI in Cybersecurity
I’m going to be talking about cybersecurity attacks and, more specifically, how artificial intelligence and machine learning algorithms can bring about new and innovative ways for malicious users to obtain altered data. Ai and machine learning affect various aspects of cybersecurity. But the ones we’ll be focusing on today are the ways that A.I. and machine learning impact the concept of social engineering and data integrity.
Impact of A.I. in Cyber Security
First, we’ll be looking at how much the cybersecurity field relies on A.I. concepts. According to Cisco’s 2018 annual cybersecurity report, 32 percent of Chief security officers are completely reliant on artificial intelligence. Similarly, machine learning is incredibly reliable, with 34 percent of organizations relying completely on it, even for companies who do not completely rely on these topics. As you can see from this graph from Cisco’s annual cybersecurity report, they still use it for a significant part of their systems.
Artificial intelligence plays a major role in cybersecurity; being able to automate processes reliably can save time and money as monitoring and decisions are made by A.I. allow workers to shift their focus on other aspects of their work. However, the nature of artificial intelligence also brings about new kinds of threats to cyberspace.
A.I. in Social Engineering
Getting phishing and scam emails regarding password changes, login information requests, and payment information is very common. Other times you might get a call from someone requesting the same type of information. But oddities and inconsistencies in the speech patterns can make it very easy to tell when it’s a machine talking. There’s currently a sample of a phishing email of attackers posing as the university management system currently on screen. The email has no personalization to the user receiving it, and companies make it known that they do not send out links regarding logging or personal information.
As convincing as some of these may be, individuals who are well aware of these practices can attack them with ease as they often have patterns and giveaways. But how can you detect if someone’s voice is not who they appear to be? Is it possible to be talking to someone you feel you trust, someone you know, or someone you think is from a reliable source or company but is just a simulation of said people’s voices? Can human speech be simulated to such accuracy that a machine can have a meaningful conversation with a person who isn’t aware of the situation?
Dessa, Joe Rogan, and Real Talk
Dessa, a company specializing in machine learning, has recreated podcast host Joe Rogan’s voice user utilizing voice clips from various sources, including his podcast. They developed a speech deep learning system, Real talk, which makes this possible. The speech is far from robotic. It flows naturally and has human life expressions like deep breath and exclamations in places where it would make sense for a human to do this.
Distinguishing the A.I.’s boys from Rogan’s is seemingly impossible. They have commented on how the scary connotations of software like this exist, saying how the software system would be capable of producing a replica of anyone’s voice provided that sufficient data becomes available.
A.I. Driven Fraudulent Phone Calls
The problems that software like bases and Real Talk can bring to the table when it comes to cybersecurity are non-trivial. Voice algorithms could mimic family members, employers, clients, and service providers to take crucial information from their victim. Though this kind of attack from such advanced A.I. seems futuristic and not something someone in the current age would need to worry about, there are reports of it already affecting people and businesses. In 2019 the Israel National Cyber Doctorate issued a warning regarding attacks in which an artificial intelligence program was used to impersonate company executives. These A.I.’s instructed employees to make malicious money transactions and perform other acts on the company’s network.
Machine Learning and Data Integrity
Training a machine-learning algorithm to study data such as images can have a huge impact on the development of future products. Google’s self-driving car, for example, uses machine learning developed by the company Deep mind. One of the software features is to determine what is a pedestrian and what isn’t a pedestrian. Earlier this year, DeepMind reported improvements made to the system in which the system decreased in false positives in determining what objects were not pedestrians by 24 percent. As one can imagine, telling what is and isn’t a pedestrian is incredibly important for a self-driving car to determine. But what happens when a machine-learning algorithm gets altered and the system is not learning as intended? Having vulnerabilities in the machine learning system, which costs us the data being learned to become compromised, has a huge impact on cybersecurity measures and modern technology.
Adversarial Attacks on Machine Learning Systems
the definition of adversarial attacks: Techniques used to trick and mislead systems through malicious and misguiding inputs. An adversarial attack can wrongly teach a machine what an object is. For example, if an algorithm has been taught what humans look like and that system is attacked, it can be slowly taught that certain pictures show humans displaying other objects. Maybe it’ll see a picture of a human after being attacked and identify it as a chair, a bear, or a frog. Whatever it is, the machine perceives the object as if it’s not accurate to what it should perceive; the entire system is compromised. Depending on the context that this system is being implemented, the result of an attack like this can be disastrous.
We already talked about self-driving cars that use machine learning to determine what a pedestrian is. If this system were attacked and taught that pictures of pedestrians were depicting green traffic lights, the result could very well be a tragedy and irreparable damage. It is crucial for the data collected by these machine systems to be as reliable and as accurate as possible.
Who Do These Issues Affect?
So who gets affected by these issues? Everyone gets affected. In terms of social engineering, everyone is at risk of getting their likenesses used as scam family members or co-workers, as discussed in the A.I. simulation section of this presentation. Likewise, somebody is very likely to get tricked into thinking they are talking to someone in their family or somebody from work. In terms of data integrity, everyone is also very much at risk of getting harmed by vehicles or other heavy machinery that could very well operate on a wrongly trained system with the wrong data on how to do their job. Beyond these two aspects of cybersecurity, a multitude of services that people use every day, such as online banking systems, online marketplaces, and phone applications, incorporate A.I. and machine learning and deal with similar kinds of sensitive data.
So how do we go about moderating this from a developer’s perspective in terms of keeping their integrity intact? It’s always important to maintain a secured and well-monitored work environment. Relying 100 percent on A.I. or relying 100 percent on anti-viruses when handling security doesn’t create the most versatile or resilient environment. There is a multitude of security measures available to the general public as many of these security measures and systems should be applied as possible to significantly decrease the chances of attacks on machine learning systems or prevent them entirely.
For the everyday person who uses technology often, maybe someone who talks on the phone after talking to family members or somebody who is often making business calls, or maybe somebody who is often making public announcements through the P.A. system in such a manner that their voices can easily be recorded or collected for data. It’s always important to have an idea of who is listening and what you are saying. As Dessa said, stealing someone’s voice relies entirely on how much data the voice cloning system is presented with; your voice is part of your identity. It can be used for malicious purposes that can affect you and the people around you.
In conclusion, due to how new and innovative A.I. and machine learning are currently, taking the correct precautions will take some time to get used to, and for some, it will not be intuitive. But just like with other aspects of cybersecurity-related issues, the risks are there. And everyone who engages with technology often should be made aware of how technology evolving creates more security concerns.