For years, artificial intelligence (AI) has been improving cybersecurity tools. Machine learning algorithms, for example, have improved network security, anti-malware, and fraud-detection software by detecting anomalies far faster than humans. AI, on the other hand, has created a threat to cyber security. Threats utilizing AI include brute force, denial of service (DoS), and social engineering attempts.
The hazards of artificial intelligence to cyber security are predicted to rise significantly as AI products become more affordable and widely available. You can, for example, mislead ChatGPT into producing malicious code or a message from Elon Musk asking for donations.
You can also use a number of deepfake tools to create surprisingly convincing fake audio tracks or video clips with very little training data. There are also growing privacy concerns as more users grow comfortable sharing sensitive information with AI.
AI, or Artificial Intelligence, is the creation of computer systems capable of performing activities and making judgments that normally require human intelligence. It entails the development of algorithms and models that allow machines to learn from data, recognize patterns, and adapt to new information or situations.
To put it simply, AI is the process of training computers to think and learn like humans. It enables machines to process and analyze massive amounts of data, find patterns or anomalies, and make predictions or judgments based on that data. AI can be applied in a variety of applications, including image and audio recognition, natural language processing, robotics, and cybersecurity.
Overall, AI aims to mimic human intelligence to solve complex problems, automate tasks, and enhance efficiency and accuracy in different fields.
The Opportunities And Threats of Implementing AI in Cybersecurity
Artificial Intelligence (AI) has become a powerful tool in many fields, including cybersecurity. With the rise of cyberattacks, businesses and organizations are looking to AI to help defend their systems and networks. However, as with any technology, AI also comes with risks and opportunities that must be carefully considered.
Despite the risks, there are also many opportunities for using AI in cybersecurity. One of the biggest advantages is the ability to automate tasks that would otherwise be time-consuming for humans. For example, AI can quickly analyze large amounts of data to identify potential threats and alert security teams.
AI can also be used to improve incident response times. By using AI to detect threats and automate responses, security teams can respond more quickly to potential attacks. This can help minimize the damage caused by cyberattacks.
Another opportunity is the ability to use AI to detect and prevent insider threats. By analyzing patterns of behavior, AI can identify employees who may be engaged in malicious activities such as stealing data or compromising systems.
One of the biggest risks of using AI in cybersecurity is the potential for bias. AI algorithms are only as unbiased as the data they are trained on. If the data used to train the AI is biased, the resulting algorithm will also be biased. This could lead to false positives, false negatives, and other errors that could compromise cybersecurity.
Another risk is that cyber criminals could use AI to launch more sophisticated attacks. For example, AI could be used to create more realistic phishing emails that are harder to detect. This could lead to an increase in successful attacks and data breaches.
AI has the potential to revolutionize the field of cybersecurity, but it also comes with risks that must be carefully considered. Organizations should be aware of the potential biases in AI algorithms and take steps to ensure they are using unbiased data to train their AI systems.
Despite the risks, the opportunities presented by AI in cybersecurity are significant. By using AI to automate tasks and improve incident response times, organizations can better defend themselves against cyberattacks. As with any technology, the key is to use AI responsibly and with a clear understanding of the potential risks and benefits.
Introduction to Deep Learning and Cybersecurity
While deep learning is well-established in data science, it may be finally finding its footing in cyber security, thanks to a variety of technological advances, trends, and advancements. Cyberattacks and data breaches are constantly increasing, with attacks increasing by more than 15% in 2021 compared to the previous year.
Experts believe that ransomware and social engineering scams are becoming more common, owing to IT flaws such as misconfigured networks, poor maintenance habits, human mistakes, and a variety of unknown IT assets. Organizations, however, can begin to take a more proactive approach to cyber defense thanks to developments in deep learning.
Deep learning (DL) is a subset of machine learning (ML), and is able to learn and improve on its own by examining computer algorithms. Deep learning uses artificial neural networks that are designed to imitate how humans think and learn. Until recently, neural networks were limited by computing power and thus were limited in complexity. But now, advancements in big data analytics have permitted larger, more sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans.
Existing cyber security solutions fail to address the growing dynamics of modern cyberattacks, particularly in detecting new threats, analyzing complex vectors and events, and an inability to scale to the sheer volume of attacks. Applying deep learning in cyber security can eliminate many of these problems with new approaches and methods, being applied to DDoS detection, identifying behavioral anomalies, detecting malware and botnets, and voice identification.
Deep Learning Improves on Machine Learning
Machine learning has always been seen as an innovative solution to protecting cyber assets. But ML tools can potentially be reverse-engineered to create bias or vulnerability that lowers the effectiveness of its defenses. Hackers can even use their own ML algorithms to infect a cyber security solution with false data sets, for example.
Deep learning, on the other hand, circumvents the need for data scientists to manually feed data sets. DL models are able to process massive volumes of raw data that are used to automatically train the cyber security system. DL neural networks are trained to become autonomous and don’t need human oversight and intervention. Over time, DL is able to more accurately identify highly complex patterns from large data sets than ML, and do it much faster.
What’s even more interesting about deep learning in cyber security is its ability to proactively identify and stop attacks before they happen. Most cyber tools are reactionary and rely on specific indicators of a compromise to detect a threat. They generally only recognize threats they already know about, but they’re not effective against unknown or zero-day threats.
Deep learning algorithms use deep neural networks to “think” like a human brain and can adjust themselves to the data properties they are trained on. That makes it easier for it to adapt automatically to the massive volume of threats out there. While ML requires too much human intervention to move fast enough, DL continues to evolve and learn over time to pre-emptively recognize threats it has not seen before and prevent them from taking effect.
DL can be very effective for intrusion detection and prevention (ID/IP), where it detects malicious network activity and prevents bad actors from accessing a network. In the past, machine learning was used for these types of defenses, but ML algorithms tended to generate too many false positives, which in turn made it more difficult for security teams to root out the real problems. DL neural networks can make ID/IP systems smarter by analyzing traffic more accurately to differentiate good activity from bad.
The application of DL offers three key advantages for cyber security teams.
- Simple: unlike machine learning, DL greatly simplifies the feature creation process, replacing complex, highly technical data pipelines with simpler, more easily trainable models. This allows cyber teams to offload more of their work, and DL can be trained for learning specific features, helping to detect unknown attacks such as zero-day malware.
- Scalable: Typical ML algorithms require the storage of all data points in memory, which is difficult to achieve when massive datasets are in play. This makes ML less able to improve performance with lots of data, and thus cannot scale. Deep learning, conversely, can be trained on datasets of different sizes and can iterate over smaller data batches. The models are better fitted to large datasets and scale much easier.
- Reusable: DL models can be re-trained when new data is introduced without having to start over from the beginning. They are better for continuous online training, which is vital for large production models. DL models can also be repurposed so previous work can be reinvested into more robust and powerful models.
Examples of NLP Applications in Cybersecurity
Even with many cyber security solutions that are AI-powered, they require human intelligence; and are not automated at their core. Typically, in cybersecurity, AI technology is used for IT asset inventory, intrusion detection/IoC detection, control effectiveness, breach risk prediction, and incident response. One thing that differentiates CyberStrong as a great example of an Integrated Risk Management solution is that it utilizes Natural Language Processing (NLP).
NLP is categorized as a subset of Machine Learning (ML) and has excellent applications for cyber security professionals seeking to improve their compliance processes continuously. Leveraging NLP has allowed us to deliver an advanced automation use case we call Cyber Risk Automation – eliminating the manual effort for assessments by up to 90% and delivering millions in cost savings for organizations across the Global 500 and more.
As the branch of AI-based deep learning that deals with the interaction between humans and computers using natural everyday language, NLP offers a wealth of capabilities to augment human ability. NLP in risk and compliance can identify overlaps in standards and frameworks and data from an organization’s tech stack, and threat feeds to identify vulnerabilities in your security infrastructure.
NLP’s ultimate objective is to “read,” decipher, and understand language that’s valuable to the end-user. In CyberStrong, NLP supports the need for automation across two of the most menial processes in risk and compliance: framework cross-walking and making security telemetry actionable from a risk and compliance perspective.
CyberStrong’s patented NLP technology makes sense of all the data coming out of a security tech stack, showing where and how various tools and solutions achieve compliance across standards. As a mode of AI, NLP also improves over time by learning from itself to become more efficient and enhance its cybersecurity processes. The automation of assessments is achieved by mapping telemetry to controls to operationalize threat and vulnerability information in real-time.
In automating the cross-walking process before unseen in the industry, the NLP engine identifies keywords in telemetry that map to specific controls and control actions. Currently, the process of cross-walking in many cybersecurity solutions is manual and inexact.
Organizations can make some use of their vulnerability information in many other integrated risk management solutions. Still, it typically requires the use of multiple, segmented products, resulting in siloed information that can be difficult to explain, much less navigate and maintain accuracy. CyberStrong’s AI solves this issue and is capable of harmonizing across all frameworks and standards.
In addition to this, CyberStrong will soon be able to map multiple control actions to describe a specific control and automatically investigate if compliance requirements are met across other controls or frameworks. The continuous training of the NLP enables true harmonization across frameworks at the assessment level.
There are two main drivers nurturing the human-machine teaming in cybersecurity activities, and language is the main tool for both of them:
- Communication — people started communicating with machines through constructed language (e.g. programming languages, etc.) but are increasingly using the natural language to do it (e.g. chatbots, virtual assistants, etc.)
- Automation efficiency — may be considered through implementation of technologies such as robotic process automation (RPA) or AI workers; the language, whether formal or natural, keeps its role as main interface.
To stress this idea, here are some NLP tasks that may be considered in creating threat or attack tools, or in defending humans and machines:
- Language Analytics: language modeling, sentiment analysis, text classification, named-entity recognition, natural language inference, relation extraction, semantic parsing, co-reference resolution, entity linking, relational reasoning, semantic composition, language identification and translation, entity and information extraction, intent detection and classification, stance and fake news detection, rumor detection, hate speech detection, clickbait detection, abuse detection.
- Language Generators: question-answering systems, text and dialogues generation, text summarization, slot filling for knowledge base population tasks, scripts and programming code generation.
These tasks should be generally considered in a more complex context, where they can (automatically) use one each other capabilities as well as other AI tools, such as sound and voice processing and generation, image processing, etc.
How AI is Used in Automated Incident Analysis And Response
Organizations must respond quickly and efficiently to reduce damage and prevent future attacks as cyberattacks become more complex. Many organizations are looking to artificial intelligence (AI) to automate their cybersecurity incident response in order to accomplish this.
AI can detect and respond to cyber threats more quickly than traditional approaches. It can swiftly and accurately analyze enormous amounts of data, find anomalies, and notify security personnel to potential dangers. AI can also be used to automate processes like banning malicious IP addresses, quarantining contaminated files, and password resets.
AI can also be used to automate the investigation process. It can collect and analyze data from multiple sources, such as network logs, emails, and user activity, to identify patterns and detect suspicious activity. This can help security teams quickly identify the source of an attack and take appropriate action.
AI can also be used to identify potential weaknesses in an organization’s security posture. It can analyze the network and identify areas where security can be improved, such as outdated software or unpatched systems. This can help organizations prevent future attacks by addressing potential vulnerabilities before they are exploited.
By automating the incident response process, AI can help organizations respond quickly and effectively to cyber threats. This can help organizations reduce the cost and disruption caused by cyberattacks, and protect their data and systems from future attacks.
Cybersecurity incidents are on the rise, with a growing number of organizations falling victim to malicious actors. As a result, organizations are increasingly looking for ways to improve their incident response times. One way to do this is through the use of AI-driven automation.
AI-driven automation can be used to automate many of the manual processes associated with incident response. This can include automating the collection of data from various sources, such as logs and network traffic, as well as automating the analysis of this data. By automating these processes, organizations can reduce the time it takes to detect and respond to incidents.
AI-driven automation can also be used to automate the triage process. This involves using AI algorithms to analyze the data collected from various sources and determine the severity of the incident. This can help organizations prioritize their response efforts and reduce the time it takes to respond to incidents.
Finally, AI-driven automation can be used to automate the response process itself. This can include automating the deployment of security patches, the implementation of containment measures, and the investigation of the incident. By automating these processes, organizations can reduce the time it takes to respond to incidents and limit the damage caused by them.
Overall, AI-driven automation can be a powerful tool for improving incident response times. By automating the collection, analysis, triage, and response processes, organizations can reduce the time it takes to detect and respond to incidents. This can help organizations limit the damage caused by malicious actors and protect their data and systems.
As cyberattacks become increasingly sophisticated, organizations must adopt advanced technologies to protect their data and networks. Artificial intelligence (AI) is emerging as a powerful tool for cybersecurity incident response, offering a range of benefits to organizations.
AI can help organizations detect, respond to, and mitigate cyber threats more quickly and effectively. By leveraging AI-driven automation, organizations can identify threats and respond to them in real-time, allowing them to take immediate action to protect their networks. AI can also be used to analyze large amounts of data, such as logs and network traffic, to identify patterns and anomalies that could indicate a potential attack.
AI can also help organizations prioritize threats and allocate resources more efficiently. By using AI to analyze the severity of threats, organizations can quickly identify the most pressing issues and allocate resources accordingly. This helps organizations respond to threats quickly and effectively, minimizing the impact of an attack.
Finally, AI can help organizations improve their overall security posture. By leveraging AI-driven analytics, organizations can identify potential vulnerabilities and take steps to address them before they are exploited. This helps organizations stay ahead of the curve and reduce the risk of a successful attack.
Overall, AI is an invaluable tool for cybersecurity incident response. By leveraging AI-driven automation, analytics, and vulnerability identification, organizations can detect, respond to, and mitigate cyber threats more quickly and effectively. As cyberattacks become increasingly sophisticated, organizations must adopt advanced technologies such as AI to protect their data and networks.
As organizations increasingly rely on technology to conduct business, the need for robust cybersecurity incident response plans has become increasingly important. However, integrating artificial intelligence (AI) into these plans can present unique challenges.
AI has the potential to greatly improve the speed and accuracy of incident response, as well as reduce the time and resources required to respond to threats. However, the complexity of AI can make it difficult to integrate into existing incident response plans.
One of the biggest challenges is ensuring that AI-based solutions are able to detect and respond to threats in a timely manner. AI systems must be able to identify and respond to threats quickly and accurately, or they may not be able to prevent or mitigate the damage caused by a cyberattack.
Another challenge is ensuring that AI-based solutions are able to accurately detect and respond to threats. AI systems must be able to distinguish between legitimate and malicious activity, and be able to accurately identify and respond to threats. If an AI system is not able to accurately identify and respond to threats, it could lead to false positives or false negatives, which could lead to serious security incidents.
Finally, AI-based solutions must be able to scale to meet the needs of an organization. As organizations grow, their incident response plans must be able to scale to meet the increased demand. AI-based solutions must be able to quickly and accurately detect and respond to threats, regardless of the size of the organization.
Integrating AI into cybersecurity incident response plans can be a complex and challenging process. Organizations must ensure that their AI-based solutions are able to detect and respond to threats quickly and accurately, and that they are able to scale to meet the needs of the organization. By taking the time to ensure that AI-based solutions are properly integrated into incident response plans, organizations can ensure that they are better prepared to respond to threats.
How AI is Changing the Grand Landscape of Threats
AI has been around since the early days of computing. The term “Machine Learning” was coined by Arthur Samuel in 1956, who developed a checkers-playing program that could improve its performance through experience. Over time, improvements were made to AI, resulting in the development of ChatGPT (Generative Pre-Trained Transformer), which is actively being used by cybercriminals. Unfortunately, this has opened everyone’s eyes to how “good” AI can be for bad actors.
For example, in December 2022, an anonymous poster claimed that ChatGPT wrote an info stealer application that searches for specific files, zips them, and sends them home. Similarly, in the same month, a threat actor dubbed USDoD posted a Python script, which he emphasized was the first script he ever created. It was made by ChatGPT. Furthermore, everyday bad actors are using ChatGPT to craft believable phishing emails. This raises concerns about the authenticity of people we interact with online, especially with the potential integration of deepfakes with AI Natural Language Processing.
To protect our environments, we need to add AI to our toolkit to identify threats faster and respond automatically. However, it all comes down to people, processes, and technology. We need to foster a culture of security and awareness within our organization, educate our employees on security best practices, and identify weaknesses in our security positions. Additionally, we should implement MFA and Conditional Access, secure and protect our on-premise and cloud-based applications, and continuously monitor for security risks.
Many vendors are already integrating AI solutions into their products. For example, ConnectWise announced its intent to integrate OpenAI ChatGPT into its Automate RMM platform. Sophos Intercept X has implemented Deep Learning to detect malware, while KnowBe4 is providing on-demand webinars and training to educate on the capabilities of ChatGPT.
To achieve a Zero Trust environment, we should focus on the business outcomes and design from the inside out. We need to determine who/what needs access, inspect and log all traffic, and implement identity management policies to prevent unauthorized access to our protected surface. Finally, we need to monitor and maintain our protect surface and set up alerting and logging of flows into and out of it.
AI has both positive and negative implications for our cybersecurity. While it can be used to identify threats faster and respond automatically, it can also be used by bad actors to craft believable phishing emails and integrate deepfakes with AI Natural Language Processing. However, by fostering a culture of security and awareness, identifying weaknesses in our security positions, and implementing MFA and Conditional Access, we can add AI to our toolkit and achieve a Zero Trust environment to protect our environments.
Companies are a long way from handing over all their Cyber Security services entirely to AI. But it can take over limited, tedious, and repetitive tasks to relieve the burden on Cyber Security personnel.
For instance, email filters and warnings, automatic malware identification and threat detection. Phishing via email is still one of the biggest Cyber Security threats and AI can be used to find, highlight, and remove suspicious emails.
Reliance on AI is on the increase. It must be, to keep up with the increase in speed and quantity of Cyber Security events. AI is excellent at detecting and managing known threats, handling large quantities of data and can manage vulnerability and security events in real-time. In many cases, it can respond quicker and more effectively than humans. It is immune to “threat alert fatigue”.
AI works well as a “cyber assistant”, working with humans and performing tasks that relieve the pressure on cyber security teams. By filtering and removing false positives an AI system can be used to ensure that humans are not inundated with information of low importance.
Using Machine Learning techniques, Artificial Intelligence can learn from previous data and threats to better prepare for new ones. AI can identify and recognize patterns, understand what constitutes regular usage and quickly identify suspicious activity.
AI is also useful for Vulnerability Management. Thousands of new vulnerabilities are identified every year, and this amount of information can be unmanageable by humans. AI can be used to prevent and manage threats quickly and enable companies to identify suspicious activity and react almost instantly, enabling a defense against attacks (known as “zero-day” attacks) that have never been encountered before.
There are many more tasks that AI systems can perform or assist with, but there can be disadvantages to using AI to manage parts of a Cyber Security strategy.
It can be very expensive to implement and may not be a viable solution for smaller businesses. AI systems are not infallible and may be tricked into incorrect behavior where more rigid systems would not be.
Companies moving to use AI systems may find that they must change some of their working practices to not generate false positives or introduce bias.
One final consideration is that the reverse of AI in Cyber Security is Cyber Security for AI. Artificial Intelligence systems can be as vulnerable to attack as any other system and AI is only as clever as the data that is fed into it. By manipulating that data, attackers may be able to trick the AI into behaving against its intended design, giving false positives or bypassing security.
How to protect AI from attack is still a new concept, but policies and standards are being developed on how to secure AI systems, such as by the Brookings Institution and the ETSI Industry Specification Group on Securing Artificial Intelligence.
The hypothetical next step for AI is Artificial General Intelligence (AGI), the type of AI that can understand and learn as well as any human. AGI is either ten years away, a hundred years away or impossible to attain depending on who you talk to.
In the nearer future, AI will take over more tasks from humans, and any company that uses any form of modern technology will find themselves virtually unable to operate without some form of AI to help and protect them.
The law, in the form of policy and regulation, will eventually catch up with AI, in the way that it has with Information Technology and Cyber Security.
The continued improvement and utilization of chatbots such as ChatGPT and Bard, and their potential for harm, is much speculated on. Chatbots are already able to communicate conversationally with thousands of users each day about almost any subject known to man.
ChatGPT has caused a sensation since its launch. Although it is still capable of making mistakes or misinterpreting requests, many in the industry see it as the way that we will use the internet in the future. And the technology industry, which is traditionally sceptical about AI in general, has become almost obsessed with the potential security concerns around chatbots using machine learning to develop and instigate cyber-attacks.
On the other hand, it has also been suggested that chatbots could generate beneficial code, that could be created quickly to counter an urgent threat or neutralize a virus.
Artificial Intelligence already plays a significant part in Cyber Security, and on its current path will take over more tasks and decision-making from humans. It will be a long time before it is smart enough to do everything unattended, but with each new technological breakthrough, we come closer to that possibility.