Depending on the point of view, the implementation of Artificial Intelligence (AI) and Machine Learning (ML) could lead towards either positive or negative effects, in regard to cybersecurity. With the use of AI, the algorithms that are utilized could be trained, based on what data is fed into it, to assess and respond to a possible situation, such as a breach. Throughout this article, the positives and negatives of utilizing AI, for cybersecurity, will be examined.
What is Artificial Intelligence (AI)?
Essentially, artificial intelligence is a type of intelligence that is expressed and demonstrated by machines rather than a conscious (natural intelligence) organism, such as a human. As a discipline, artificial intelligence has been a topic since the 1950s, and has since been studied very heavily and has grown exponentially. With projects, such as Open AI, led by Elon Musk, the study and utilization of artificial intelligence have not slowed down and will constantly see more usage within the daily lives of people and businesses. To learn more about artificial intelligence, check out the article written by BuiltIn and the excerpt provided by IBM Cloud Learn Hub.
Existing Challenges in Cybersecurity
As a result of the COVID-19 pandemic, the probability of a breach has increased despite the fact that there have been numerous advancements in the realm of cybersecurity. Some of the issues that are prevalent include:
- Manual threat detection: Manual event monitoring becomes more difficult due to geographic distance. To successfully track incidents across borders, cybersecurity experts must resolve infrastructure gaps.
- Pinpointing hackers’ IP addresses: This can be costly and time-consuming, resulting in further attacks going unnoticed.
- Geographically distant IT systems: Companies can only fix problems after they have occurred. Security analysts face a difficult task in predicting threats before they arise.
- Reactive nature of cybersecurity: Hackers use a variety of tools, including Virtual Private Networks (VPNs), proxy servers, Tor browsers, and others. These tools assist hackers in remaining anonymous and undetected.
AI and Cybersecurity
Fortunately, cybersecurity is one of the leading usages for artificial intelligence. Through a report gathered by Norton, they showed that the average data breach would cost a business roughly four million dollars, based on their recovery expenditure. One other thing that is fascinating in the report is that it shows that those companies need roughly 7 months to recover from it. As a result, the dependency of AI becomes more prevalent so that companies can avoid such negative outcomes.
With things like AI, machine learning and other forms of threat intelligence can help identify a pattern to put forth a security system, based on what was learned through data. Another remarkable thing about the utilization of AI is that an organization can drastically minimize their incident response times and can easily comply with the best possible security protocols.
How AI benefits Cybersecurity
Threat Hunting
To recognize risks, traditional security strategies depend on signatures or indications of compromise. This technique may be effective against previously experienced threats, but it is ineffective against threats that have yet to be identified.
Around 90% of threats can be detected using signature-based techniques. Traditional techniques can be replaced with AI to improve detection rates by up to 95%, but there would be an influx of false positives. Combining conventional approaches with AI will be the perfect option. This will lead to a 100% identification rate and fewer false positives.
By incorporating behavioral analysis, companies may use AI to improve the threat hunting process. For example, by processing large amounts of endpoint data, AI models can be used to create profiles of any application within an organization’s network.
Vulnerability Management
In 2019, there were 20,362 new vulnerabilities recorded, up 17.8% from 2018. On a regular basis, organizations struggle to prioritize and handle the vast number of new vulnerabilities they find. Traditional vulnerability management approaches typically wait for high-risk vulnerabilities to be exploited before addressing them.
Although conventional vulnerability databases are important for managing and containing known vulnerabilities, AI and machine learning techniques such as User and Event Behavioral Analytics (UEBA) can analyze baseline activity of user accounts, endpoints, and servers to spot anomalous behavior that could indicate a zero-day unknown breach. This can help companies secure themselves even before bugs are publicly disclosed and patched.
Data Center
Many critical data center processes, such as backup power, cooling filters, power consumption, internal temperatures, and bandwidth utilization, can be optimized and monitored with AI. AI’s calculative abilities and continuous monitoring capabilities reveal which values will increase the efficiency and security of hardware and infrastructure.
Furthermore, AI can lower the cost of hardware repairs by alerting you when the equipment needs to be repaired. These warnings allow you to fix your equipment until it breaks down completely. In reality, after introducing AI technology inside data centers in 2016, Google announced a 40% reduction in cooling costs and a 15% reduction in power consumption at their facility.
Network Security
Creating security policies and recognizing an organization’s network topography are two time-consuming elements of traditional network security.
- Policies: Which network connections are valid and which should be investigated further for malicious activity are identified by security policies. These policies can be used to successfully implement a zero-trust model. Given the vast number of networks, the real difficulty is in developing and implementing policies.
- Topography: Most companies don’t use the same naming conventions for their applications and workloads. As a result, security teams must devote a significant amount of time to deciding which workloads belong to which application.
By learning network traffic trends and suggesting both practical grouping of workloads and security policy, companies may use AI to enhance network security.
Limitations of Utilizing AI for Cybersecurity
One must note, despite all of the many pros to the utilization of AI, there are some setbacks/limitations that could prevent it from being more popular for security measures, for now:
- Resources: To develop and maintain AI systems, businesses must spend a significant amount of time and money in resources such as processing power, memory, and data.
- Hackers can utilize AI: In order to make their malware immune to AI-based security tools, attackers test and refine it. Hackers use existing AI tools to create more sophisticated attacks and attack conventional security systems as well as AI-enhanced systems.
- Neural Fuzzing: Fuzzing is the practice of analyzing software with vast quantities of random input data in order to find flaws. Neural fuzzing makes use of artificial intelligence to easily evaluate a large number of random inputs. Fuzzing, on the other hand, has a positive side. Hackers may use the power of neural networks to gather information about a target system’s vulnerabilities. Microsoft devised a method for implementing this strategy in order to better their applications, resulting in more stable and difficult-to-exploit code.
- Data Sets: Learning data sets are used to train AI models. Security teams need access to a variety of data sets containing malicious codes, malware codes, and anomalies. Some businesses simply lack the resources and time to compile all of these detailed data sets.
Cybersecurity Companies Utilizing AI
There are numerous companies that are involved with utilizing AI’s as a part of their cybersecurity services. The following are some of the most recognizable, innovative and respectable companies in this specific space:
- SentinelOne: SentinelOne is a comprehensive endpoint security framework that protects against a variety of attacks and protects users over the threat lifecycle. Threats are detected and contained autonomously using SentinelOne’s Static AI and Behavioral AI engines. The platform prevents, detects, and even reverses attacks ranging from ransomware and phishing emails to trojans and vulnerabilities hidden in documents and files.
- Shape Security: For businesses in retail, banking, government, technology, and travel, Shape Security provides software that combats impersonation attacks such as fake accounts, password stuffing, and credit application fraud. Shape’s machine learning models were given access to data that resembled that of fraudsters, allowing the device to learn what human behavior looks like in the face of fraud. Enterprise Defense and Blackfish, two of the company’s solutions, use AI to distinguish between real and fake users and then block, redirect, or flag the fraudulent source.
- Palo Alto Networks: Palo Alto Networks is a major player in the cybersecurity industry, with more than 50,000 customers spanning sectors in more than 150 countries. Its solutions address a broad range of requirements, including firewalls, cloud security, threat detection, and endpoint security.
- Fortinet: Fortinet offers security solutions for any aspect of an organization’s IT infrastructure. A majority of Fortune 500 businesses use Fortinet’s cybersecurity products, which range from network and web application security to threat detection and secure unified access. FortiWeb, the company’s AI-based product, is a web application firewall that detects threats using machine learning and two layers of statistical probabilities.
- Jask: Jask provides an autonomous interface for task automation in security operations centers, giving analysts more time to analyze high-priority warnings. The software provides them with complete visibility through the organization, including qualitative data, allowing them to more quickly identify threats.
- Sovereign Intelligence: Sovereign Intelligence’s AI platform is actively analyzing data and comparing it to non-traditional data sources in order to provide businesses with early warning of vulnerabilities. Furthermore, the company’s cyber threat feeds are actively monitoring malicious actors and using artificial intelligence to anticipate threats and recommend appropriate defenses.
- Cynet: Cynet 360 employs artificial intelligence to provide a comprehensive cybersecurity protocol. From continuously scanning for vulnerabilities and suspicious activities to coordinating response efforts if a device is compromised, AI is present in nearly every phase of Cynet’s security services.
- Darktrace: Darktrace has helped thousands of businesses in the number of industries track and combat cyber attacks in real-time through more than 30 offices around the world. The AI platform from Darktrace analyzes network data to perform calculations and spot patterns. Data is used by machine learning technology to assist organizations in detecting deviations from normal behavior and identifying threats.
- Crowdstrike: Crowdstrike is a company that makes cloud-based endpoint security applications. Falcon, the company’s network, provides consumers in sectors such as banking, healthcare, and retail with the prevention, visibility across endpoints, and proactive threat hunting. Beyond basic identification, the Falcon platform investigates threats automatically, removing the guesswork from threat analysis.
- Anomali: Anomali offers technology to help companies and organizations detect suspicious behavior in their networks before it becomes a problem. Security teams and researchers will use the company’s threat intelligence solutions to detect threats and competitors, as well as partner with other companies and share their findings.
- TruU: Via a combination of biometrics, communications, and attitudes, the TruU Identity Platform automatically tracks and analyzes transactions between the digital and physical worlds to identify security risks. TruU’s AI gathers information based on the above parameters and alerts users to any unusual behavior that does not match a trustworthy user profile.
- White Ops: Machine learning and artificial intelligence are used by White Ops to detect security breaches and avoid malicious bot activity. White Ops detects the existence of automated bot traffic and automatically blocks it until it threatens financial transactions or back-end infrastructure by using artificial intelligence to bind millions of points at once.
- Cylance: Cylance is an AI platform that predicts and protects against file-less attacks, ransomware, and zero-day payload execution, helping to avoid threats before they can cause damage. Cylance’s technology works by analyzing billions of file samples for threats, determining if a threat exists, and quarantining contaminated files. Small businesses to enterprise and service companies in a variety of sectors, including oil, finance, government, healthcare, and retail, are among our clients.
- CUJO AI: Machine learning is used by CUJO AI’s framework to evaluate and protect anything from browsers to smartphones to IoT devices. Malicious malware and phishing schemes are stopped before they can compromise a network, thanks to algorithms that identify patterns and anticipate attacks ahead of time.
- Vectra: The Cognito Platform from Vectra employs artificial intelligence to detect cyber threats in real-time. Cognito automates activities that are usually performed by security analysts and significantly decrease the amount of work needed to conduct threat investigations by combining human intelligence, data science, and machine learning. Data scientists, network security engineers, researchers, and UI designers make up Vectra.
- Symantec: Symantec protects clouds, endpoints, and infrastructures for states, civilians, enterprises, and organizations. The company’s intelligence tools help security teams stay on top of emerging threats and put steps in place to counter them. Its threat analysis tool, which is hosted in the cloud, assists teams in prioritizing problems and providing more background when reacting to threats.
Conclusion
Artificial intelligence and machine learning have the potential to increase security while also making it easier for cybercriminals to break into networks without the need for human interaction. Any business could suffer serious consequences as a result of this. If you want to reduce losses and remain in company, you should invest in some kind of cyber security.
Michael is an Information Technology consultant, with a focus on cybersecurity. Every day, Michael strives to learn something new, with an aim to share it with you all!
