AI: The Friend & Foe of Cybersecurity

If you have been left astonished by the advancements made by Artificial Intelligence (AI) in the recent past, then you aren’t the only one. Humanity hadn’t anticipated such developments in the field of AI at this rapid rate, leave alone the impact it has had on numerous industries.

AI has undoubtedly transformed our societies, economies, and entire industries in the months that have passed by. Among these industries, while most have enjoyed a positive change, there is one that now has a mixed bag of emotions: Cybersecurity.
With the technological landscape evolving at a fast pace, AI and Cybersecurity have found themselves closely intertwined with each other. But what alarms me isn’t how AI is assisting in strengthening cybersecurity walls, but rather how it is also opening the floodgates for serious cybersecurity breaches.
In this article, we will be exploring both the aspects of how AI is being a friend as well as a foe of cybersecurity. But prior to that let me give you a brief about how AI affects the cybersecurity sector in the first place.

How does AI impact the cybersecurity sector?

In the recent past, AI has had a notable impact on the cybersecurity sector. For an AI to have an impact on cybersecurity, it uses training data for refining its algorithms, which then allow it to respond to different situations accordingly.

Now AI and ML along with threat intelligence have the ability to recognize patterns when scanning through data. Upon analyzing past data, AI and ML tend to recognize patterns in data breaches and thus improve the cybersecurity of the firm in the days ahead. But at the same time, democratized access to AI is becoming a pain for cybersecurity experts. The ‘good guys’ do not have a monopoly on AI innovations, this allows malicious hackers to use AI models to generate attacks more efficiently at lower costs, and without human intervention at times.

Let us first explore the ways in which AI has proven to be a friend of cybersecurity.

AI: A Friend of Cybersecurity

Reports by Norton suggest that on average, a data breach typically costs around $3.86 million to recover. At the same time, the company takes around 200 days to recover from a cybersecurity attack, that is indeed a lot! The need for the cybersecurity sector to be strengthened has never been this essential earlier.
Here are 3 ways how AI assists cybersecurity in the modern-day: -

1. Threat detection – Earlier in the day, threat detection techniques involved the use of signatures and compromised indicators to predict an impending attack, but this isn’t effective in the current day scenario. These methods cannot detect threats that haven’t yet
   been discovered.
   While these signature-based techniques are believed to detect around 90% of all threats, the application of AI takes this up to 95%. But, the optimal way of inculcating AI is by combining it with traditional practices of threat detection. This would lead to a detection rate of almost 100% with minimal false positives. A company also has the luxury to integrate behavioral analysis in this process and take the threat detection process a notch higher.

2. Managing Vulnerabilities – If you look at this literally, the meaning seems to be quite clear that techniques are employed to manage any detected vulnerability, and this is what traditional cybersecurity systems do. But, with the onset of AI and ML, you now have the power to protect your firm from any impending attack, even before a vulnerability has been officially detected.
   The traditional methods wait for malicious attackers to exploit a high-risk vulnerability and then come into action to neutralize them. But, AI and ML employ techniques such as User and Event Behavioural Analytics (UEBA). This technique has the ability to analyze the behavior of all accounts on the server and detect any anomaly.
   This phenomenon helps firms in protecting themselves even before a vulnerability ticket has been raised.

3. Automated Incident Response and Remediation – Imagine if your company could immediately respond to a cyberattack, thus minimizing the harm caused by the cyberattack! Well, the application of AI and ML into the cybersecurity ecosystem performs this function exquisitely. When AI and ML are integrated into the current cybersecurity system, they enable the automation of incident response processes, reducing the impact of security breaches.

As AI and ML instantly come into action, they isolate all compromised systems, contain threats from being further spread, and begin remediation processes without the need for human intervention. With the security system responding immediately to any and all threats, the attackers now have a very small window to execute their plans, thus minimizing potential damages.
The cherry on top here is, in these situations, not only does AI protect the system but learns from the incidents and optimizes itself to perform better in any future security breaches.
You now have an idea of how AI and ML integration into the cybersecurity ecosystem is the future. But this is a double-edged sword. Let us explore its flip side.

AI: A Foe of Cybersecurity

You will now discover 3 ways in which AI is proving to be a hindrance to cybersecurity in the current scenario: -

1. Sophisticated Cyber Attacks – The advancement of AI benefits both the protectors and the destroyers. Cybercriminals use AI and ML to leverage AI-powered tools to automate their attacks, making these cyber-attacks much more sophisticated. The key threat of these attacks is, as they are AI-driven, these attacks can adapt and learn from past defensive responses to evade detection and exploit vulnerabilities. This makes it difficult for cybersecurity experts to deal with the attacks.

   1. Data Poisoning and Model Manipulation – We earlier discussed how AI and ML are trained on existing data to perform better. While this is a boon, it is a curse too. Hackers tend to have the means to manipulate this input data and thus compromise the integrity of these models during the training phase. Now with the models compromised, the results produced are biased leading to erroneous predictions and incorrect security assessments.

2. AI-Driven Phishing and Social Engineering – Since the advancement of AI and ML, attackers are now making use of these tools to conjure up phishing and social engineering attacks. This is how they do it- AI-powered chatbots are now increasingly successful at impersonating individuals and trusted sources, this makes phishing attempts much more convincing and difficult to detect. At the same time, these cybercriminals use AI information available on social media to craft personalized phishing messages, skyrocketing the success of such attacks.

Final Word

There is no denial to the fact that the introduction of AI and ML to the realm of cybersecurity has opened a number of doors to our future. Now unfortunately not all these doors lead to a better tomorrow, but that is the challenge we have to ride through.
The introduction of AI possess a number of challenges but it is our responsibility to address them through continuous research, and innovation. Only a collaborative effort from all fronts of cybersecurity experts can lead to a sound defense mechanism that can withstand the ever-evolving landscape of AI-driver cyberattacks.