In the ever changing world of cybersecurity, staying on top of trends and new technologies is crucial to keep your networks safe and secure. Cyber attacks are happening at an unprecedented pace and cost organizations an enormous amount of money every year.
As outlined in the Cost of Data Breach Report for 2021 by IBM, businesses with operating AI security and automation had the biggest positive cost impact. The IBM report found an “80% Cost difference where security AI and automation was fully deployed vs. not deployed”. The report shows that these businesses with AI security fully deployed saw breach costs of approximately $2.9 million, compared with $6.7 million for businesses without security AI and automation.
Artificial Intelligence (AI) is quickly becoming an important tool in cybersecurity. AI is not just data analysis, an AI system by definition learns and gets better and more efficient at its assigned task as time goes on. This makes it uniquely qualified to identify and mitigate the almost infinite and ever changing cybersecurity landscape.
Right now, AI is the ideal solution for businesses looking to keep their systems secure. The IBM Cost of Data Breach Report 2021 has identified an 80% cost difference where security AI and automation was fully deployed vs. not deployed. It highlighted that organisations with fully deployed security AI and automation experienced breach costs of $2.90 million compared to $6.71 million in organisations without security AI and automation. In the very near future, it is very likely that not only will AI be ideal, but also absolutely necessary.
As of now, no other tool can do the work as efficiently and quickly as AI. This article examines the benefits that AI provides to security professionals tasked with keeping their organizations safe. Let us first examine some of the key tasks that AI can accomplish more efficiently than conventional software.
AI Learns More Over Time
For years, hackers have been trying to exploit a certain vulnerability, and if successful, we’re able to keep improving on their processes to make their subsequent attacks stronger. AI takes huge strides in preventing this from happening. AI, by definition, learns over time and gets better and more efficient over time.
AI, and a subset of AI, Deep Learning, recognizes patterns in a network’s behavior over time, and any deviation in the pattern can be flagged as a possible security breach or attempted breach. If an attack does happen to be successful, the AI will learn from the attack and flag any similar future attacks as soon as they arrive.
With so much data being transferred within and to and from an organization’s systems, the ways that an attacker can get in are endless. All of that data must be protected from malicious actors and software. AI is the perfect tool to constantly monitor this flow of data for unusual activity. Any threats can be quickly identified. Compared to conventional software, the learning capabilities of AI give it a leg up in efficiently monitoring all of an organization’s data.
AI automates repetitive learning and discovery through data. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks. And it does so reliably and without fatigue. Of course, humans are still essential to set up the system and ask the right questions.
Perhaps the best known application of AI in cybersecurity is in authentication. Tools such as facial recognition and CAPTCHA provide bulletproof authentication when paired with AI. As the password becomes outdated and is the most common weak point in a system, AI powered authentication is quickly becoming the norm at most organizations.
Deep learning artificial neural networks are showing promising results in analyzing HTTPS network traffic to look for malicious activities. This is very useful to deal with many of the most common cyber threats such as SQL injections and DOS attacks. The AI quickly learns the patterns used by these types of attacks, and constantly stays one step ahead of them.
Tracking and analyzing user activities and behaviors is an important security practice for any organization. It is much more challenging than recognizing traditional malicious activities against the networks since it bypasses security measures and often doesn’t raise any flags and alerts.
For example, when insider threats occur and employees use their legitimate access in malicious intent, they are not infiltrating the system from the outside, which renders many cyber defense tools useless against such attacks.
User and Entity Behavior Analytics is a great tool against such attacks. After a learning period, it can pick up normal employee behavioral patterns and recognize suspicious activities, such as accessing the system in unusual hours, and even different mannerisms in the way a user moves the mouse around the screen, that possibly indicate an insider attack and raise alerts.
AI Identifies Unknown Threats
From phishing, malware, and social engineering, the amount of attacks numbers in the hundreds of millions every year. A human being simply cannot identify them all. AI is the best tool to learn to identify the attacks in real-time as they come in.
Early detection of threats is crucial in preventing a loss of data or a security breach. AI constantly scans the entire system for anything that could be a possible threat. Conventional cybersecurity simply cannot do that as efficiently as AI due to the constantly changing nature of the security landscape.
Traditional malware solutions such as the antiquated firewall detect malware by using a signature-based detection system. A database of known threats is run by the company which updates it frequently to incorporate new threats that were introduced recently. While this technique is efficient against these threats, it struggles to deal with more advanced threats, and almost by design, the system must fail and let in a bad actor somewhere in order to obtain the need to update the system to detect the threat in the future.
Deep learning algorithms are capable of detecting more advanced threats and are not reliant on remembering the known signatures and common attack patterns. Instead, the AI systems learn the algorithms used by bad actors and can recognize suspicious activities that might indicate the presence of an attack or malware.
AI Provides Better Vulnerability Management
Using AI, weak points are identified much quicker than with conventional methods. Constant analyzing and learning by AI cybersecurity systems manages your vulnerability much more quickly than can be done otherwise.
Due to the constant changing nature of the threats, it is difficult to prioritize different security tasks. What is important today may not be as important tomorrow. AI is a powerful enough tool when it can detect all types of possible attacks simultaneously and is not prone to human error or negligence. Simply put, AI is unrivaled in a security setting.
The Future of Cyber Security
Cybersecurity in the age of the Internet of Things can’t be tackled in the same way as that of conventional computing environments. There are just too many variables and too many different devices. Imagine monitoring security alerts for millions of globally detected devices, especially considering many of the devices in the IoT are quite simple and lack the computing power to run conventional cyber security software systems.
This is where AI and machine learning become necessary. ML can make up for the lack of security teams or high powered conventional software systems. AI can efficiently help discover devices and hidden patterns in security attacks while processing those huge amounts of data. ML can help monitor the incoming and outgoing traffic for any deviations in the behavior in the IoT ecosystem. If a threat or anomaly is detected, alarms are then immediately sent to security administrators warning them about the suspicious traffic.
AI and ML can also be used to build extremely lightweight endpoint detection technologies, an example of how the extreme efficiency of AI makes up for its extra expenses. This will be an indispensable solution, especially in situations where IoT devices lack the computing and processing power and need behavior-based detection capabilities that aren’t as resource intensive.
AI and ML technologies can also be a double-edged sword. Threat actors have weaponized AI to automate things like target selection or attack timing to reduce the likelihood of detection. Deep Fakes, human impersonation, and AI-powered password guessing all pose a threat. The misuse of AI and ML has been a worrying trend for a long time that seems set to grow in step with its adoption across the business world. Enterprises need to take particular notice of any potential malicious exploitation of their own AI systems. For example, cybercriminals have been able to copy the ML model for Proofpoint Email Protection and manipulate it to allow malicious emails to pass through filters.
To sum up, organizations must carefully consider the security implications to have a successful and robust set of cyber security protocols in the very near future. As connected devices take over legacy technology, it becomes increasingly difficult to fight cyber threats without the intervention of advanced AI. We need computers that protect themselves, without worrying too much about their ability to spy on your business or cause harm by partnering in crimes that can be committed against supply chains, vendors, partners, or customers. AI has proven worthy in many different areas of our lives. Going forward, AI will continue to transform cybersecurity. AI will support businesses to predict chances of security breaches. It’s not hard to imagine how AI can make a significant contribution to strengthening the future of our security.
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References:
https://www.balbix.com/insights/artificial-intelligence-in-cybersecurity/
https://mobilesyrup.com/2019/02/06/google-machine-learning-gmail-spam-filter/
https://www.ceps.eu/artificial-intelligence-and-cybersecurity/
