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As technology advances and becomes a more integral part of the modern world, cybercriminals will learn new ways to exploit it. The cybersecurity sector must evolve faster. Could artificial intelligence (AI) be a solution for future security threats?

AI programs can make autonomous decisions and implement security efforts around the clock. The programs analyze much more risk data at any given time than a human mind. The networks or data storage systems under an AI programs protection gain continually updated protection thats always studying responses to ongoing cyber-attacks.

People need cybersecurity experts to implement measures that protect their data or hardware against cyber criminals. Crimes like phishing and denial-of-service attacks happen all the time. While cybersecurity experts need to do things like sleep or study new cybercrime strategies to fight suspicious activity effectively, AI programs dont have to do either.

Advancements in any field have pros and cons. AI protects user information day and night while automatically learning from cyber attacks happening elsewhere. Theres no room for human error that could cause someone to overlook an exposed network or compromised data.

However, AI software could be a risk in itself. Attacking the software is possible because its another part of a computer or networks system. Human brains arent susceptible to malware in the same way.

Deciding if AI should become the leading cybersecurity effort of a network is a complicated decision. Evaluating the benefits and potential risks before choosing is the smartest way to handle a possible cybersecurity transition.

When people picture an AI program, they likely think of it positively. Its already active in the everyday lives of global communities. AI programs are reducing safety risks in potentially dangerous workplaces so employees are safer while theyre on the clock. It also has machine learning (ML) capabilities that collect instant data to recognize fraud before people can potentially click links or open documents sent by cybercriminals.

AI decision-making in cybersecurity could be the way of the future. In addition to helping people in numerous industries, it can improve digital security in these significant ways.

Even the most skilled cybersecurity teams have to sleep occasionally. When they arent monitoring their networks, intrusions, and vulnerabilities remain a threat. AI can analyze data continuously to recognize potential patterns that indicate an incoming cyber threat. Since global cyber attacks occur every 39 seconds, staying vigilant is crucial to securing data.

An AI program that monitors network, cloud, and application vulnerabilities would also prevent financial loss after a cyber attack. The latest data shows companies lose over $1 million per breach, given the rise of remote employment. Home networks stop internal IT teams from completely controlling a businesss cybersecurity. AI would reach those remote workers and provide an additional layer of security outside professional offices.

People accessing systems with AI capabilities can also opt to log into their accounts using biometric validation. Scanning someones face or fingerprint creates biometric login credentials instead of or in addition to traditional passwords and two-factor authentication.

Biometric data also save as encrypted numerical values instead of raw data. If cybercriminals hacked into those values, theyd be nearly impossible to reverse engineer and use to access confidential information.

When human-powered IT security teams want to identify new cybersecurity threats, they must undergo training that could take days or weeks. AI programs learn about new dangers automatically. Theyre always ready for system updates that inform them about the latest ways cybercriminals are trying to hack their technology.

Continually updating threat identification methods mean network infrastructure and confidential data are safer than ever. Theres no room for human error due to knowledge gaps between training sessions.

Someone can become the leading expert in their field but still be subject to human error. People get tired, procrastinate, and forget to take essential steps within their roles. When that happens with someone on an IT security team, it could result in an overlooked security task that leaves the network open to vulnerabilities.

AI doesnt get tired or forget what it needs to do. It removes potential shortcomings due to human error, making cybersecurity processes more efficient. Lapses in security and network holes wont remain a risk for long, if they happen at all.

As with any new technological development, AI still poses a few risks. Its relatively new, so cybersecurity experts should remember these potential concerns when picturing a future of AI decision-making.

AI also requires an updated data set to remain at peak performance. Without input from computers across a companys entire network, it wouldnt provide the security expected from the client. Sensitive information could remain more at risk of intrusions because the AI system doesnt know its there.

Data sets also include the latest upgrades in cybersecurity resources. The AI system would need the newest malware profiles and anomaly detection capabilities to provide adequate protection consistently. Providing that information can be more work than an IT team can handle at one time.

IT team members would need the training to gather and provide updated data sets to their newly installed AI security programs. Every step of upgrading to AI decision-making takes time and financial resources. Organizations lacking the ability to do both swiftly could become more vulnerable to attacks than before.

Some older methods of cybersecurity protection are easier for IT professionals to take apart. They could easily access every layer of security measures for traditional systems, whereas AI programs are much more complex.

AI isnt easy for people to take apart for minor data mining because its supposed to function independently. IT and cybersecurity professionals may see it as less transparent and more challenging to manipulate to a businesss advantage. It requires more trust in the automatic nature of the system, which can make people wary of using them for their most sensitive security needs.

ML algorithms are part of AI decision-making. People rely on that vital component of AI programs to identify security risks, but even computers arent perfect. Due to data reliance and the newness of technology, all machine learning algorithms can make anomaly detection mistakes.

When an AI security program detects an anomaly, it may alert security operations center experts so they can manually review and remove the issue. However, the program can also remove it automatically. Although thats a benefit for real threats, its dangerous when the detection is a false positive.

The AI algorithm could remove data or network patches that arent a threat. That makes the system more at risk for real security issues, especially if there isnt a watchful IT team monitoring what the algorithm is doing.

If events like that happen regularly, the team could also become distracted. Theyd have to devote attention to sorting through false positives and fixing what the algorithm accidentally disrupted. Cybercriminals would have an easier time bypassing both the team and the algorithm if this complication lasted long-term. In this scenario, updating the AI software or waiting for more advanced programming could be the best way to avoid false positives.

Artificial intelligence is already helping people secure sensitive information. If more people begin to trust AI decision-making in cybersecurity for broader uses, there could be potential benefits against future attacks.

Understanding the risks and rewards of implementing technology in new ways is always essential.

Cybersecurity teams will understand how best to implement technology in new ways without opening their systems to potential weaknesses.

Featured Image Credit: Photo by cottonbro studio; Pexels; Thank you!

Zac is the Features Editor at ReHack, where he covers tech trends ranging from cybersecurity to IoT and anything in between.

Originally posted here:

Can We Trust AI Decision-Making in Cybersecurity? - ReadWrite