How AI And ML Can Help In Cybersecurity Risk Management
How AI And ML Can Help In Cybersecurity Risk Management
Table of contents
- Cybersecurity threats and risks faced by organizations currently
- Five of the other major threats to organizational information include the following:
- AI And ML Use Cases In Cybersecurity
- Some use cases of AI-integrated cybersecurity risk management include:
- What is cyber risk quantification (CRQ), and how can it help?
- What is network intrusion detection and prevention?
- Future of AI in cybersecurity risk management
With the rising number of cybercrimes, the need for advanced AI and ML techniques to protect sensitive information online is increasing too. Businesses increasingly rely on AI-integrated systems to address problems in decision support, facial recognition, financial risk management, etc. The use of AI in cybersecurity can no longer be overlooked.
The ability of AI algorithms to analyze enormous amounts of data makes them a viable solution required to advance the processes of cybersecurity threat detection and prevention successfully. In most cases where traditional systems are the only barricade of protection, the damage has already been done by the time humans detect a cyberattack. Deploying advanced AI mechanisms can help detect and protect from an attack faster, decreasing the response time and damage.
In this article, we will look at how organizations can use AI for security risk management and what can be expected in the near future.
Cybersecurity threats and risks faced by organizations currently
The list of security threats and risks faced by organizations is endless. Cybersecurity is one of the biggest challenges of the digital age. Network security teams need to stay one step ahead of the attackers as there is an unprecedented escalation in the number of cybercrimes against organizations every year.
Organizational information is at risk of phishing attacks, cross-site attacks, brute force attacks, and so on.
Five of the other major threats to organizational information include the following:
1. Distributed Denial of Service (DDoS)
A DDoS attack aims to make a server, service, or network temporarily or indefinitely unavailable by using several infected systems as attack traffic sources. This is a serious threat to internet service providers and other consumer service-based companies. According to a report by Cisco, there will be nearly 15.4 million DDoS attacks by 2023.
Malware is a malicious software program in the form of a virus, spyware, worm, or other threats that executes unauthorized actions on the victim's computers. These actions can include stealing, erasing, or encrypting sensitive data, monitoring users’ behaviors, etc. The type of malware used depends on the type of organization targeted.
Ransomware is a type of malware that encrypts and keeps users’ computers locked in return for a ransom amount. It is similar to a DDoS, except for the difference that in DDoS, no users can access the infected service or network. While a ransomware attack may be limited to some users in a network, others might be able to access services without any disruption or awareness of the attack.
4. Advanced Persistent Threat (APT)
An APT attack comprises an unauthorized user gaining access to the most sensitive and confidential information without being detected. The main purpose of such attacks is to steal and distribute highly protected information, such as in the defense, manufacturing, or finance sectors. Unlike the attacker often getting in and out of the network to remain undetected by an intrusion detection system, the attacker gains continuous access in an APT.
As the name suggests, a botnet is a network of interconnected devices acting as bots that are infected and remotely controlled to carry out unauthorized actions and distribute spam to a user’s computer.
AI And ML Use Cases In Cybersecurity
AI and ML play a pivotal role in security risk management and primarily comprise learning from the past data and creating invaluable predictions for future threats. Detection of ongoing cyber attacks also comes as an advanced application of AI. It is preferred over manual detection because it helps save valuable time and minimize the damage caused by malicious software.
Some use cases of AI-integrated cybersecurity risk management include:
1. Risk Reduction
AI algorithms can analyze past data to identify user behavior patterns associated with security risks and help detect them early on. This helps evaluate risky behaviors and prevent them from reducing further risks.
2. Fraud Detection
Fraud detection requires intense data mining and analysis to find patterns in data that lead to fraud. AI can help reduce the workload in fraud detection systems to catalyze these processes and increase the reliability and accuracy of results. Financial institutions and insurers can greatly benefit from these AI-integrated fraud detection systems.
3. Data Classification
AI algorithms can help analyze, categorize, and monitor access to heaps of data and information available as per existing patterns. All these activities help protect these data sets from any cyberattack better.
4. Threats Analysis And Management
AI/ML models can help enhance threat analysis and management processes by creating real-time prediction models. These models help decision-makers to address potential security risks in time. They are also helpful for ensuring uninterrupted operations in organizations and protecting the interests of their stakeholders.
All these use cases have benefited from AI-integrated risk management systems to ensure
- Quicker response
- Reduced risks
- Prioritized threats
What is cyber risk quantification (CRQ), and how can it help?
Cyber risk quantification (CRQ) is essentially a process of estimating the financial and operational impacts of a potential cybersecurity attack. It helps evaluate every vulnerability and potential threat in an existing cybersecurity system. CRQ helps turn this information into numbers that are useful for decision-making at the senior management level.
CRQ is the process of representing risks in business terms.
It is imperative to strengthen the objectivity and accuracy of an organization’s cybersecurity risk assessment and understand the effectiveness of the risk mitigation strategies. All these benefits also help gain a competitive advantage as the higher cybersecurity performance also translates to uninterrupted operations.
AI can help create advanced modeling techniques for cyber risk quantification.
An organization can select from any available cyber risk quantification models based on the type of data they deal with.
What is network intrusion detection and prevention?
Intrusion Detection and Prevention System (IDPS) is software that monitors network traffic to help identify and alert about any harmful or malicious activities encountered. Powered with AI algorithms, a conventional intrusion detection system can be made more flexible, adaptable, and faster at computing. AI allows an IDS to recognize patterns from past records and also detect new patterns.
AI techniques help networks to understand and detect intrusions exactly as they happen in real-time. This quick response can help reduce the damage to a network that could have been manifold when relied just on the traditional methods of intrusion detection.
The primary objectives of AI-integrated IDS include:
- Privacy protection
- Risk minimization
- Dynamic threat response
- Virus tracking
Future of AI in cybersecurity risk management
Currently, AI systems can predict and detect future and ongoing cyber attacks from the historical data that is fed to them. With the risks in cybersecurity increasing every day, it will take arduous efforts to ensure protection from every new threat. According to online statistics, the AI is expected to reach $66.22bn in this market, at a compound annual growth rate of 24.2% from 2022 to 2029. We can expect AI/ML methodologies will dramatically speed up cybersecurity processes with greater precision, while also lowering the costs related.