cybersecurity ai

Will AI and Machine Learning Help or Hurt Cybersecurity?

cybersecurity AIAI and machine learning are part of the everyday lexicon. We, as consumers, see their impact everywhere, from using digital assistants to viewing recommendations from your favorite e-commerce sites. They are also impacting cybersecurity. AI and machine learning in cybersecurity aren’t new concepts, and their adoption continues to accelerate.

The question is — will they help or hurt cybersecurity? Some may quickly say they’ll only help, as they remove much of the human element. Humans, after all, are more prone to error than sophisticated technology. However, we shouldn’t be so quick to dismiss the human role in protecting and securing networks and data. Perhaps, the two can work in harmony.

AI and other advanced technologies have been heralded as the next generation of cybersecurity. Except they aren’t foolproof. Data breaches still occur, and ransomware incidents continue. The reality is that cybersecurity and its effectiveness don’t rely on one solution to be successful. Technology and technical folks that leverage people skills and basic methodology are the best defense against cyberattacks.

In this post, we’ll look at how AI and machine learning are helping and hurting cybersecurity. Then, we’ll look at ways technical teams can marry these tools with their own skills to produce the best security posture.

The Positive Impacts of AI and Machine Learning on Cybersecurity

AI and machine learning are powerful assets. They continue to improve what they can do and how fast they can do it. They also eliminate some of the remedial, mundane, and transactional tasks that burden technical roles. When deployed with a strategic approach, they can identify vulnerabilities and shorten incident response time.

Here are the positive ways they are impacting cybersecurity.

Detection of Fraud and Anomalies

One of the most common ways that AI and machine learning support cybersecurity is how good they are at detection. AI engines are really good at recognizing patterns, and most scams include these. Detecting fraud and anomalies is a big deal for any organization, and AI and machine learning can deliver lots of details on these. It also takes the pressure off of solely using humans for detection because the sheer volume of this is overwhelming.

Filtering of Email Spam

Phishing email scams remain one of the most lucrative channels for hackers to gain access to networks. Data revealed that 2021 saw the highest number of phishing attacks ever, making them three times more common than just two years prior.

Phishing attacks are also becoming more sophisticated with the help of social engineering. No matter how much you train your users on the “signs” of phishing, sometimes these emails look very genuine (as opposed to those with misspellings and sentences that don’t make sense).

So, AI and machine learning become a tremendous help here to minimize these making it to an inbox. You can use them to create defensive rules to filter out messages that appear suspicious for several reasons.

Identification of Botnets

Machine learning algorithms facilitate the detection of and prevent bot attacks. They can also identify user behavior patterns to determine undetected attacks, which would be near impossible without the technology, with a very low false-positive rate.

Management of Vulnerabilities

Vulnerabilities are the thorn in every cybersecurity team’s side. Managing these manually or with the use of tools is cumbersome and difficult. AI solutions make this much easier. It has the ability to look for potential vulnerabilities with an analysis of user behavior, servers, endpoints, and more. It works as a nice complement to other vulnerability detection strategies.

Better Anti-Malware Utilization

AI can support antivirus software to do its job better. It aids in the detection of good and bad files. As a result, it can discern new malware forms even if they haven’t been seen prior. It’s not a sure thing and works in harmony with traditional methods. It’s not a replacement but augments the solutions already in place.

Prevention of Data Leaks

Data leakage is a significant risk for any organization, so fortifying prevention of this is a good use case for AI. It works to decipher specific data types in text and non-text documents. Algorithms learn how to detect sensitive information types by searching data in images, voice records, and videos.

Augmentation of SIEM and SOAR

Machine learning uses security information and event management (SIEM) and security orchestration, automation, and response (SOAR) tools to boost data automation and intelligence gathering. In this capacity, machine learning can discern suspicious behavior patterns and then automate a response depending on the input.

These examples show the possibilities of AI and machine learning in cybersecurity operations. Implementing these things into your security protocols can immediately improve traffic analysis, detection, intrusion prevention, and more. So, your technical team should welcome them with open arms.

That may not be the case. Remember, new technology means a change to how your people operate today. They may be resistant or resentful, even though these tools will help them! To usher in any kind of change, your team has to have solid people skills that allow them to see this as an opportunity. After all, they often want to be the smartest person in the room — not technology.

In fact, they may fear the introduction of AI and machine learning, erroneously believing it will take over their jobs. That’s far from true, but it’s a hurdle you must keep in mind. It’s a major theme in my book, The Smartest Person in the Room.

Now that we’ve looked at the positives of AI and machine learning in cybersecurity, it’s time to look at the other side.

The Negative Impacts of AI and Machine Learning on Cybersecurity

AI and machine learning are great tools for those in cybersecurity. Hackers also use them. In these cases, there are negative impacts of technology. Here are some of the ways criminals use it.

Data Gathering

Machine learning is an approach that hackers can take for better victim profiling when it comes to social engineering and phishing attacks. They can leverage this information to deploy more sophisticated attacks that could trick your users.

Ransomware Proliferation

Ransomware is a growing problem. The number of ransomware attacks in 2022 thus far is 236.1 million. They impact both small businesses and enterprises with often disastrous consequences of data loss.

Unfortunately, cybercriminals are using AI to launch ransomware attacks with great success, including the Colonial Pipeline attack that shut down operations for six days.

Algorithms Craft Too Real Phishing Scams

Hackers can utilize machine learning algorithms to develop fake messages that look all too real. The goal is, of course, to steal credentials and access networks. These algorithms do a better job than human-created ones.

Deepfakes with Voice Phishing

Scammers are now voice phishing with machine learning-generated deepfake audio technology. One of the most prominent, Baidu’s “Deep Voice,” only needs to hear a voice for a few seconds to reproduce it accurately.

Hiding Malware

Machine learning can hide malware that tracks node and endpoint behavior. It builds patterns that mimic legitimate network traffic. It can also integrate a self-destructive mechanism in malware to amplify the speed of an attack. These trained algorithms extract data faster than any human could, making it challenging to prevent.

Passwords and CAPTCHAs

Cybercriminals use machine learning to analyze password data sets to guess them better. It elevates password-cracking tools to the degree that no human can reach. This kind of risk makes it even more important to deploy multifactor authentication (MFA).

As you can see, the advantages that AI and machine learning provide those protecting data also benefit hackers. Technology is neither good nor bad. It depends on the application. As we’ve seen for decades, there is always a “dark side” to technology.

So, what do these implications mean for the human element of cybersecurity?

AI, Machine Learning, and the Human Element

Arming your technical folks with the best tools is only part of winning the cybersecurity war. First, they must embrace the technology for what it can do rather than see it as a threat. That’s the crux of how these things should work together. Yet, it’s often the hardest part because technical professionals often have fixed mindsets that don’t make space for innovation or new ideas.

You’ll have to break this cycle by honing their people skills and helping them realize that new tools are for their benefit. Additionally, your people have to understand that technology works both ways. In the end, it’s not going to be solely a battle of AI vs. AI. The human element must be there, and it has to be one that’s passionate about keeping data secure.

Making these changes and adjustments won’t happen overnight. Nor will the implementation of AI and machine learning. If you can ensure these things are occurring in parallel, you’ll have the greatest success.

For help on the topic of people skills and growth, you’ll find lots of answers, tips, and strategies in my book, The Smartest Person in the RoomGet your copy today!