Why Do You Need Artificial Intelligence in Cybersecurity?
Amid rising cyber crimes, organizations are trying to protect digital data from unauthorized access, and implementing robust data security measures to –
a. Stay compliant with industry and government regulations.
b. Save organizational data from malicious actors.
c. Avoid data breaches, litigation, and reputational damage.
d. Protect intellectual property from getting compromised.
However, AI’s role in data security is largely overlooked. AI and ML algorithms have the potential to effectively identify malware, online fraud activities, and unauthorized intrusions.
51%[i] of enterprises primarily rely on AI for threat detection, leading prediction, and response.
Though many industry leaders have begun to leverage AI to protect their businesses against growing cyber threats, there’s still a long way to go to realize that AI has a vast scope to help combat cyberattacks.
So, here’s an article about how AI has an extended role in detecting cyber risks and identifying malware.
Let’s begin!
Benefits of AI in Cyber Security
1. Detection and Prevention of Cyber Attacks
AI algorithms can analyze vast amounts of data and identify patterns of behavior that are indicative of cyber attacks. By monitoring network traffic, AI can detect and prevent attacks in real time.
2. Threat Intelligence
AI can help organizations stay up-to-date with the latest threats and vulnerabilities by analyzing data from various sources, including social media, forums, and other open-source intelligence.
3. Automation
AI can automate many cybersecurity tasks, such as patch management, vulnerability scanning, and incident response. This can save organizations time and money while increasing the speed and accuracy of their response to threats.
4. Fraud Detection
AI can help detect fraudulent activity, such as phishing attempts or credit card fraud, by analyzing patterns and behaviors associated with these activities.
5. Predictive Analytics
AI algorithms can also analyze data and predict future threats, allowing organizations to take proactive measures to prevent cyber attacks.
6. Advanced Malware Detection
AI can detect and analyze complex malware that may go undetected by traditional antivirus software.
7. Improved Network Security
Machine learning and deep learning help AI models recognize the network patterns present in an organization. AI technology can recognize these patterns and analyze if there are any deviations or security incidents.
8. Proactive Vulnerability Management
AI can leverage its capabilities to access business systems more efficiently than cybersecurity personnel can. Thus, reducing the workload on security professionals and enabling businesses to focus on important security-related tasks and better management of vulnerable aspects.
Check the Security Level of Your Network
Examples of AI in Cybersecurity
Custom officers in the US use a security screening AI framework[ii] called Automated Virtual Agent for Truth Assessment in Real-time (AVATAR) to distinguish suspicious passengers.
Devised by the Department of Homeland Security, this AI model screens the body motions and looks of every individual passenger. Artificial Intelligence, Machine Learning, and Big Data technologies are embedded in this framework to test the face and voice tone of passengers.
Also, below are just a few examples of AI-powered cybersecurity solutions being used today to protect organizations from cyber threats.
Palo Alto Networks
Palo Alto Networks is an AI-powered cybersecurity platform that uses machine learning algorithms to detect and respond to cyber threats. Its algorithms analyze network traffic, user behavior, and other data to detect and respond to threats.
IBM Watson for Cybersecurity
IBM Watson for Cybersecurity is an AI-powered security platform that uses machine learning algorithms to identify and respond to cyber threats. Its algorithms analyze network traffic, user behavior, and other data to identify and deal with threats.
Amazon GuardDuty
Amazon GuardDuty is an AI-powered threat detection service that uses machine learning algorithms to analyze network traffic, user behavior, and other data to handle threats in real time.
Artificial Intelligence and Cybersecurity: Challenges & Solutions
|
Challenges |
Solutions |
|---|---|
|
Usage of adversarial techniques such as evasion, poisoning, and data poisoning to deceive or mislead the system. |
Implement adversarial training to train AI models to identify adversarial attacks using data augmentation, robust optimization, and defensive distillation. |
|
There’s a lack of interpretability because AI models are considered a “black box” since they make decisions without explaining. |
AI models can be designed to provide explanations for their decisions using techniques such as feature importance analysis, local interpretability methods, and rule extraction. |
|
There’s limited labeled training data in cybersecurity, which makes it challenging to train AI models. |
Transfer learning can help AI models learn from related tasks and leverage that knowledge to improve performance in cybersecurity tasks with limited data. |
|
AI models can exhibit bias if the training data is skewed or if the models are trained on biased data, leading to incorrect predictions and decisions. |
AI models should be trained on diverse and representative data using data augmentation, active learning, and synthetic data generation. |
Best Practices To Implement AI-powered Data Security
1. Strategize & Define Your Objectives
Define the objectives of the AI-based data security solution and identify the key areas that need to be protected. This will help you select the right technology and tools to implement the solution. Consider factors such as the complexity of the solution, the size of the data set, and the resources available to implement an AI-powered data security solution.
2. Utilize the Power of Data
Ensure you feed adequate data to your AI model. Your AI-enabled solution should have full visibility into your enterprise’s infrastructure and data landscape. Any incomplete data might cause misleading results and a consequent AI execution failure.
First, employ business experts to access detailed interpretation of your data and make sure that it is reviewed under expert supervision for skewed labels and other errors. This sample data should have a connection with the data you want to draw inferences from.
3. Unlock the Benefits of SOAR Technology
Security Orchestration, Automation, and Response (SOAR) is a platform that helps organizations streamline and automate their security operations. It can help organizations deploy AI-powered cybersecurity tools more effectively by streamlining and automating security operations and providing actionable insights and decision-making support. Here’s how –
a. Integration: SOAR platforms allow for the integration of various security tools, including those powered by AI. This integration enables automated workflows to be created that leverage AI tools to detect, investigate, and respond to security incidents.
b. Decision-Making: AI-powered cybersecurity tools generate large amounts of data, which can be overwhelming for security teams to analyze and interpret. SOAR platforms can use AI to help analyze this data and provide actionable insights to security teams, reducing the time it takes to make informed decisions.
c. Automation: SOAR platforms can automate routine security tasks, such as threat detection and response. This automation allows security teams to focus on more complex tasks and enables faster response times to security incidents.
d. Incident Response: SOAR platforms can also be used to orchestrate incident response activities. This includes automating the containment, eradication, and recovery processes, as well as providing real-time communication and collaboration between security teams.
VAPT Assessment Helped a Customer Achieve 100% Protection From Data Leakages
Key Takeaway
Artificial intelligence has evolved a lot over the years and helped IT security teams enhance their performance. But given the scale of cyberattacks in modern times, traditional security measures can no longer defend against them.
Therefore, AI gives organizations a crucial threat identification analysis to help minimize the risk of data breaches and strengthen the security posture.
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