In the modern world, cyber-security is becoming a real challenge, as government institutions, giant companies, and persons are acutely suffering due to cyber-attacks.
The truth is that today’s businesses are extensively using the cloud and various mobile apps to extend their competitiveness. Nevertheless, little do these businesses understand the level of insecurity they are exposing their information.
Big corporations around the globe are having their systems deeply embedded with security measures. However, the majority of small businesses are investing in the most cost-effective security technologies.
Despite the security measures incorporated in mobile applications and IoT technologies, it is authentic that cyber-attackers are finding new ways to crack and access this information: they are doing it frequently and more creatively and innovatively.
Businesses are losing valuable data, clients, and information from cyber-attacks.
Artificial intelligence (AI), just like cyber-attackers, popularly known as hackers, is perpetually evolving. New AI-driven security systems that can easily diagnose and prevent threats.
This way, AI is playing a very critical role in solving the cyber-security issue. Nevertheless, AI is a passage to cyber-attackers. For instance, at one time the New York Times published a report about research conducted by Chinese and American AI experts.
It was reported that the researchers programmed AI systems to perform activities like dialing phones and browsing websites – all these were done without the awareness of the operators.
Hackers robbing financial institutions such as banks are using AI-controlled client identification software to execute their nefarious activities.
Also, a study conducted by Webroot, a cyber-security company showed that over 90 percent of cyber-security experts in the United States affirm that cyber-attackers used artificial intelligence against organizations they have worked for, for their defense.
Uses of artificial intelligence in the industry are constantly increasing. Fast and perfect scores for financial institutions, modern methods of disease diagnosis and cure, and improved production technologies for engineering and manufacturing industries are some examples.
A study conducted by the MIT Sloan Management Review (MIT SMR) and the Boston Consulting Group (BCG) in 2017 discovered that approximately 20 percent of firms have adopted AI in their businesses and close to 70 percent of business executives are eyeing to adopt it in the near future.
Despite the above-mentioned benefits, there are risks associated with AI adoption. AI and machine-learning protocols use “trained” data while learning how to solve problems.
They learn, incorporate more data, and refine their methods in a repetitious way. This method poses two major challenges. First, artificial intelligence systems are commanded to perform subtractions and make decisions automatically without any human intervention.
Any manipulation in these processes can go obscure. Second, the prime models for subtraction and decision making may not be interpreted as required. It implies that even after detecting a violation, the motive behind it can remain unclear.
AI is regarded as a throughway for cyber-attackers for a number of reasons:
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In order to have a better comprehension of cyber-risks associated with Artificial intelligence, it is critical to comprehend the concept of modus operandi of the Artificial intelligence system.
For example, machine-learning protocols – which are affiliated to AI systems – operate by performing analyses of both the input and output information and applying this knowledge to twist the system for various instances.
Therefore, this protocol learns while doing then it polishes the entire method repeatedly. Concerning cyber-security, this method poses fundamental risks.
Since the artificial intelligence systems are commanded to perform subtractions and make decisions automatically without any human intervention, any manipulation and intrusion in these processes can go obscure.
The rationale for why machine learning algorithms would execute some deductions and make various decisions will not always be in line with the expectations of the system admins.
Rather, the logic of the protocol could be excessively sophisticated and hard to interpret. This implies that even after the system admins manages to uncover violations in the system, the motive behind it may stay unknown for some time.
So it is regrettable that the violation may be spurned as a minor system fault even when it can be seen vividly it was a real effort of a hacker attempting to manipulate or take full control of an AI system.
While machine learning AI-controlled systems are entrusted for taking full control/management of the physical system, the probable outcomes are tremendous such as injuries, loss, and demolition of property, or even deaths.
Artificial intelligence algorithms are usually free to the public and the program is often open-source. The protocols can be accessed easily from the internet and are passably simple to use.
These open-source programs used for lawful reasons can as well serve as a gateway and passage for hackers to perform illegal activities
While software-as-a-service is gaining popularity, malware-as-a-service is increasingly growing creating a criminal niche that favors the burgeoning of artificial intelligence security risks.
Additionally, there exists an implicit contention amongst hackers in the “Dark Web” who are battling to discover the “baddest malware.”
While some cyber-security providers are amalgamating behavioral analytics, machine learning, and numerous AI attributes in their productions, a great proportion of the anti-malware systems, however, rely upon signature-dependent recognition.
Hackers can generate viruses that conceal their genesis and their actions making it harder for recognition of their digital fingerprints using ordinary security tools.
Today, tailor-made malware capable of evading recognition by even the most powerful antiviruses are being sold on the “Dark Web.” By supplementing an AI malware kit to the malware, stealth is added keeping it informed of any updates to the anti-malware and protective software.
For instance, a botnet can be used to explain the perspective of threats in an AI system. A botnet consists of several internet-linked devices where the network is run by command-and-control software giving them the directions of their respective actions.
A botnet is a robust ammunition usually leveraged to execute DDoS attacks. Suppose a botnet is commanded by an AI protocol giving it significant liberty from human command, it would be able to trace all the threats and enhance its efficacy.
It could customize its attacking mechanism depending on the vulnerabilities it confronts on the targeted system.
Unlike the monolithic attack where the actions are the same regardless of the target, the autonomy of an AI botnet implies that every target and action is customized following the immediate instance surrounding it. Consequently, there is infiltration and manipulation of more hosts.
AI is capable of jeopardizing cyber-security ingeniously. While organizations are leveraging AI technologies in their security systems, the annexation by machines is creating security deception that is soothing IT experts into complacency.
Considering the probable vulnerabilities presented by the AI tools, it could be a deadly error. Ergo, building up AI applications must concord with building up the security.
Since there has never been a perfect AI-controlled cyber-security solution, AI can only be used as a supplement to an existing security infrastructure of an institution but not an entire substitution for the fundamental mechanism needed to prevent hackers and malware.
AI increases the adaptability, pace as well as degree of cyber-threats. The lengthy operations involved when preparing to launch a cyber-attack like penetrating through immense data volumes can actually be executed at machine speed with no hesitations.
Also, the capability to swiftly cross-examine the unstructured information enables AI malware to recognize connections that are rather faint or almost unreadable.
As algorithms are considered self-learning, they have the capacity to get “wiser” in every failure and customize the successive attacks following the intelligence they have acquired.
It is possible for cyber-attackers to automate vulnerability recognition and exploit writing. Consequently, AI algorithms can be ramped up such that they can predict the reaction of a target and behave in a manner that prevents evoking the defense mechanisms.
Also, AI algorithms have the capacity to preserve the anonymity and location of cyber-attackers in a niche where substantiating and probing criminals is a real tussle.
IT experts must be able to stop cyber-attacks 100 percent at all-time, while hackers only require to infiltrate the system one time.
Whereas artificial intelligence is striving to reduce cost and variability, and reducing errors, hackers are using AI to surmount it. Attackers are using AI to automate resource-intensive modules and divert all the controls established to block them.
As organizations expand their business, their volume and sophistication of their technologies grow as well, implying that hackers have a larger surface to exploit.
To evade attacks, businesses apply advanced technologies like AI to build preventive mechanisms instead of putting too much effort across the whole environment.
Nevertheless, the application of AI in organizational processes can manipulate the nature of cyber-threats and property that requires protection.
Increased dependence on AI technologies can create a favorable environment for hackers to compromise significant processes that affect both the running of the organization and its customer relations.
In conclusion, businesses need to advance their protection against AI-enabled attacks by adopting the two-pronged strategy.
First, they need to protect/shield their AI-enabled systems from attacks, and secondly, they need to protect both the AI-enabled systems and non-AI digital properties from AI-enabled cyber-attacks.
Organizations evaluate their application of AI and develop tough control mechanisms to prevent threats.
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