An increasing number of unknown threats are forcing the cybersecurity companies to find new approaches to data protection. Market experts are looking to the use of artificial intelligence and machine learning algorithms for cybersecurity as one of the ways to withstand modern cyber attacks.
This article focuses on how artificial intelligence (AI) and machine learning (ML) can improve cybersecurity and explains some of the challenges that development teams may face when implementing AI and ML in their security solutions.
Nowadays, companies use a great number of devices in their operations, and thus the amount of data they need to monitor is also massive. Hackers are looking for any security loopholes to exploit, and cybersecurity vendors are still losing this race.
Traditional security applications only inform about suspicious activity and known attacks that have already happened. Besides, these systems provide too many undifferentiated alerts that need to be interpreted by people. However, the increasing volume of data that needs to be analyzed makes it impossible for people to do all the monitoring.
Thus, there’s a need to computerize security analysis actions previously performed by humans. While signature-based methods are no longer effective, developers are trying to create more complicated security platforms with a new approach for cyber attack detection and response. Machine learning and artificial intelligence for cybersecurity promise to process high volumes of data to detect suspicious activity quickly.
Though sophisticated tools are used for cybersecurity protection, data breaches still happen. Moreover, there’s a risk that detection, investigation, and remediation of damage by security managers will take weeks or even months. Fortunately, cybersecurity solutions based on machine learning raise the prospect of detecting an attack in real time and reducing the time for remediation.
So what is artificial intelligence? AI is often defined as the science of making machines replicate human intelligence. AI involves a great variety of technologies, some of which already exist and others are still under development. Examples of AI applications include intelligent personal assistants like Apple’s Siri, game-playing programs like AlphaGo, and question-answering computer systems for business analytics, such as IBM Watson.
What is machine learning? ML is a subfield of AI that uses mathematical algorithms to find patterns in data and learn from those patterns just like people do. In cybersecurity, ML can detect anomalous behavior of users and systems as well as learn from existing threats and predict unknown threats. The main algorithms used in ML for cybersecurity are based on supervised and unsupervised learning.
Implementing AI and ML in cybersecurity can significantly help in developing more effective security solutions that are able to better protect companies against existing and unknown threats. AI-driven security applications can react to suspicious activity in real time and prevent attacks before they happen. Such solutions can quickly process huge unstructured and hybrid datasets. In addition, advanced technologies reduce the time for investigating attacks and produce fewer false positives. Thanks to ML algorithms, security systems can be self-learning and can augment human decision making.
Developers of many security information and event management (SIEM) applications are trying to implement machine learning. SIEM solutions include event and log management, behavioral analysis, and real-time monitoring of databases and applications. In case of suspicious activity, SIEM applications alert security managers and block access.
Unfortunately, the increased amount of data that needs to be monitored is almost impossible for people to process. And analytics based on predefined rules doesn’t meet the needs of today’s market. Nowadays, it’s not enough just to detect typical anomalies based on simple rules; security solutions should also automatically analyze a huge number of events and provide security managers with few false positives. Advanced analytics can automate the analysis of huge datasets using machine learning algorithms.
According to Gartner’s definition, advanced analytics (AA) is the autonomous processing of data with the help of AI techniques and tools that find deeper correlations, make predictions, and provide recommendations. AA uses a next-generation ML technology called deep learning. When comparing deep learning vs machine learning, deep learning algorithms can process large volumes of data (or big data) using neural networks that simulate the activity of the human brain. In the case of cybersecurity, big data means a huge number of system objects and user activity indicators, all of which are processed with advanced analytics. Big data integration in cybersecurity is only possible with the implementation of AI and ML.
Machine Learning – Existing Applications
Advanced analytics for security are implemented in user and entity behavior analytics (UEBA) solutions as additional functionality for SIEM applications. There are also stand-alone UEBA applications on the market. UEBA security software supplies companies with advanced analytics for both user behavior data and data collected from networks, endpoints, and applications. UEBA is a type of ML model that is deployed to recognize and withstand sophisticated cyber attacks.
There are two categories of advanced analytics, each providing different outputs after implementing big data and requiring different levels of human involvement: predictive and prescriptive.
- Predictive analytics predicts what will happen in the future. Using sophisticated statistics and machine learning techniques, predictive analytics analyzes historical and current data to predict what you should expect, though what actions you should take in response is up to you.
Undeniably, predictive analytics will make your security solution more effective at detecting attacks before data leakage or outside intrusion happens. However, predictive analytics will not provide you with an answer to how you should react if a client’s network is under attack.
- Prescriptive analytics answers what you should do given particular expected outcomes. Prescriptive analytics not only predicts future events but also analyzes possible outcomes and suggests what actions you should take in order to achieve the best results.
Implementing prescriptive analytics in security solutions will help you detect an attack before it happens. Moreover, a security application based on prescriptive analytics will provide a client with detailed instructions on what they should do in each particular case. For instance, if a user tries to send sensitive data to an external server, the system will advise executing a firewall rule in order to break the connection.
Currently, there are few security solutions on the market that support predictive analytics. One that does is Bottomline Technologies. However, there is no deployment of prescriptive analytics now that could empower AI to enterprise integration and automatically react to threats.
Gartner predicts the further integration of ML and AI in cybersecurity solutions within the next five years. By 2018, Gartner expects that 25% of cybersecurity solutions will involve some form of AI and ML for attack detection and response. Moreover, more than 50% of traditional security solutions will be supplied with UEBA functionality.
In recent years, the cybersecurity industry has been booming with fast-evolving startups based on AI and ML. Understanding modern threats, businesses are gladly investing in promising startups that take an intelligent approach to cybersecurity like Darktrace, CrowdStrike, Hexadite, Cylance, and Amazon Macie, which is former Harvest.AI.
Darktrace has recently raised $75 million for developing technology that uses ML algorithms to detect and stop attacks written not only by people but by machines as well. Its Enterprise Immune System is based on AI and unsupervised learning that works similarly to the immune system in the human body. The Enterprise Immune System can identify abnormal activity on a network and inform security managers about the intrusion. Apart from this, the system is also trained to take instant measures for blocking or slowing down an attack.
CrowdStrike is another cybersecurity startup that already has a billion-dollar valuation. This startup positions its product as next-generation antivirus software that ensures endpoint protection along with endpoint detection and response using supervised ML algorithms for malware detection. The company doesn’t rely solely on ML but also on signatureless AI and indicator-of-attack for preventing unknown attacks in real time.
This summer, Microsoft bought Hexadite, an AI-based startup, for $100 million. Hexadite’s technology implements AI and ML algorithms to separate false positives from real malware. The system uses advanced user behavioral analytics and security alerts from other vendors and security solutions to predict potential attacks. In case of malicious activity, the system automatically blocks activity to limits the damage.
CylancePROTECT is an endpoint protection startup that uses AI and ML algorithms to understand a hacker’s logic. Cylance implemented patented machine learning techniques that are able to detect known malware along with zero-day attacks and block their execution.
Harvest.AI was recently acquired by Amazon and became the prototype for Amazon Macie, which inherits its application of ML and AI. Amazon Macie helps companies monitor the flow of sensitive data and protect against data leakage. It processes user behavior analytics and blocks ongoing attacks before data is lost.
If you want to integrate AI in your startup, you should consider the challenges you may face in developing a UEBA solution that relies on artificial intelligence and machine learning technologies.
- Data about known malware and attacks. AI-based security solutions use big data about malicious activity to train their models. Determine how you will acquire the necessary data and from what devices you will receive data for user and entity behavior analytics.
- Enterprise security rules. If you want to deploy prescriptive analytics, you should be aware of the security rules for each particular enterprise. A system can propose effective solutions only when it fully understands the operations of a company.
- Lack of computing power. AI and ML algorithms require extensive computing capabilities. Even in the case of promising offline results, online testing of your security system may face difficulties because of a lack of computing power.
- Lack of experienced data scientists and analysts. These two types of experts are extremely necessary for developing an AI-based security solution. While data scientists should be familiar with data analysis, computer science, and statistical modeling, data analysts should have deep knowledge of mathematics and experience using analytical tools to extract insights from big data. If you’re looking for an outside vendor, find out whether the development team has previously deployed advanced analytics for cybersecurity.
AI with ML solves cybersecurity problems by computerizing the analytical process. Though there’s beena boom of AI-based security startups, none of them realizes the full potential of AI and ML for cybersecurity yet. Implementing AI and ML requires deploying advanced technologies that are not so easy to develop. Apriorit has advanced skills in cybersecurity systems along with experience in data processing and software development. Our team can help you successfully overcome all the complexities of embedding artificial intelligence in your enterprise app.
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