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Automation of security solutions using artificial intelligence and machine learning algorithms is critical to improve security posture. The reasons are many. With the increasing growth of the enterprise due to digitization, the attack surface has also grown massively. This poses a serious security challenge, and it is not humanly possible to keep track of the security threats and signals continuously and analyze them to evaluate the risks. This challenge can be fixed to a large extent through AI-based security solutions that can analyze millions of security events to detect various complex threats to mitigate breach risks. These solutions have many benefits. AI can learn continuously through machine learning and deep learning models to identify changing patterns in the network and cluster them to detect changes or incidents from baseline behaviors before initiating a response. It can track and detect unknown threats and attack vectors by monitoring network traffic, as it can handle large volumes of data. Due to its robust vulnerability management capabilities, AI-based security solutions can quickly identify security gaps and weaknesses, prioritize attacks based on severity, and take on several threats simultaneously. More importantly, the use of AI in cyber security reduces human error and negligence and offers accelerated threat detection and response. Due to the extensive use of data, AI ensures fewer false positives, empowering teams to focus on the threats that matter. Such features improve IT security posture and ensure compliance and alignment with regulatory requirements.
Artificial intelligence for cybersecurity to counter cyber threats
Here is a list of the most commonly occurring cyber threats and how AI based security solutions can help reduce their attack risks and improve cyber posture.
- Malware: Due to its ability to detect real-time threats by analyzing large data from network traffic, security logs and user behaviors, an AI solution can quickly detect and neutralize malware that otherwise would have gone unnoticed through traditional antivirus software. Even if new malware versions emerge, AI solutions can learn quickly, adapt and implement new ways to find them within the network.
- Phishing and spear-phishing: With the help of ML algorithms, AI solutions can analyze data from emails and user behaviors to detect any suspicious patterns and activities related to a phishing attack. These solutions implement threat intelligence by monitoring activities from social media and other online platforms to check for any signs of such attacks. The algorithms use this data to detect emerging threats quickly and prevent them from spreading across the network. This can block malicious links and URLs before they are even clicked or downloaded by the employee.
- Distributed Denial of Service: AI solutions can detect anomalies in network traffic to prevent a DDOS attack. These attacks are often driven through a large volume of traffic that can overpower a network or system. Through real-time network traffic analysis, AI solutions can block malicious traffic before it even reaches the target network or system. AI solutions can learn and evolve, which helps take on any new variations of DDoS attacks.
- Man-in-the-Middle: This is a serious threat to network communications. AI solutions can reduce the impacts of these attacks by identifying and blocking suspicious activities in the network traffic. ML algorithms can detect network anomalies in real-time and alert IT staff on such an attack to take remedial actions. AI solutions can encrypt network communications & implement authentication and access control protocols to protect sensitive data from unauthorized access, making it difficult for attackers to intercept traffic and modify them to their benefit.
- Advanced Persistent Threats: These threats are long-term attack campaigns with multiple stages. AI solutions can block unusual user behavioral patterns indicating such an attack. They can also implement robust data access controls and user authentication methods to ensure that only authorized users can access systems.
- Insider threats: This is a significant risk to any organization. Insiders can be employees or partners or anyone having authorized access to sensitive data in the network. They can intentionally or otherwise cause real harm to the company’s finances or operations. There are AI solutions such as User and Entity Behavior Analytics which verify user profiles using data from active directories, applications, server logs, or devices to evaluate them for risky behaviors.
- SQL injection attacks: An attack used by hackers to steal or manipulate data. AI based solutions can mitigate such risks by continuously analyzing user inputs to determine whether they are malicious query or not. If the input is flagged as malicious, the IT team is alerted to carry out further investigations. These solutions can implement user input validation and access controls to prevent attackers from injecting malicious code into databases.
- Cross-Site Scripting (XSS) attacks: This is a web application attack targeted towards data theft, defacing a website, or taking over a user account. AI solutions can prevent such attacks by blocking malicious code and ensuring it is not executed in the user’s browser. It can also implement user validation controls against specific criteria to ensure attackers do not insert malicious code into web pages.
- Zero-day exploits: Here, attackers try to take the upper hand by taking advantage of an unknown application vulnerability, and since it is unknown, there is no patch or a fix to prevent the exploit. AI based solutions can detect such exploits by ML algorithms and automating incident responses. Once detected, these solutions can trigger responses to isolate the infected system or application and alert the IT staff to take necessary action.
- Password attacks: This attack intends to get passwords by guessing or cracking them and gaining unauthorized access to resources. ML algorithms can detect unwanted behavioral patterns or unusual login attempts during non-working hours or from a remote location, flag them as suspicious behaviors, and then initiate subsequent steps to authenticate users. They can also inform users if they use weak passwords and prompt them to use stronger passwords.
- Social engineering attacks: In such attacks, cybercriminals trick users into divulging sensitive data or performing unwanted actions that can compromise an organization’s systems or data. AI-based solutions can analyze various user behavior patterns to detect any anomalies that might indicate an attack in progress. Any suspicious emails will be flagged/blocked, and the user is prompted to act cautiously.
- Fileless malware attacks: These attacks are tough to detect as they operate in the memory. AI solutions can monitor any unusual processes running in the memory or unwanted network traffic and isolate the system from the network. It can analyze command and control traffic patterns as this attack will often communicate with its server to get instructions or steal data. Such activities are immediately detected and blocked to prevent any damage.
- IoT-based attacks: IoT devices lack the security features of most normal computing devices.This makes them vulnerable to DDoS attacks, malware infections, and credential theft. AI solutions can monitor unusual network traffic or abnormal device behaviors; it monitors the devices if they send an unusual amount of data to any external server. It can analyze code running in multiple IoT devices and check for any patterns of coordinated attacks; if any such activity is detected, appropriate action is taken to prevent its spread.
- Watering hole attacks: Here, attackers frequently target the websites visited by intended victims and infect their devices with malware or steal their data. AI solutions use ML algorithms to detect such patterns by analyzing web traffic. It also checks social media activities to detect any possibilities of such attacks. Network data from multiple devices can be analyzed to check for any unwanted user behavior patterns which might give rise to large-scale watering hole attacks.
Features in AI based security solutions to detect threats
Anomaly Detection: AI based security solutions use anomaly detection features to respond to threats quickly across multiple channels, be it social media, online banking, customer support centers, etc. Even if new threats are emerging quickly, ML algorithms can learn from past security events to prepare a response to stay ahead of these threats. Anomaly detection is used by AI solutions to detect patterns of behavior that might be indicative of threats, enabling IT staff to react quickly. The number of false positives and negatives will drastically reduce – false positives are alerts caused by legitimate behavior; false negatives happen due to threats not being detected. Only those which are truly indicative of a threat are detected and notified.
Predictive Analytics: Adopting predictive analytics is the best way to proactively detect security vulnerabilities. Through predictive models, steps can be taken to prevent an attack; it is the ideal choice for fraud detection, optimizing supply chains, and customer analytics. With their ability to scale, they can be easily integrated into existing data management and analytics platforms. They also have data correlation capabilities that can check for attack patterns and provide risk scores based on security events.
Natural Language Processing: AI based solutions use NLP to enable computers to understand and process human language patterns. Various statistical and ML techniques are used to generate language text. This is used to analyze data, patterns, and characteristics of malicious code, log data, network traffic, email content, and attachments to identify and detect attack trends or new threat vectors. It can also monitor compliance by analyzing text documents such as policies and legal agreements.
Deep Learning Models: these models use neural networks to analyze data from networks to detect intrusions, which can be subtle patterns and behaviors. It can analyze code and patterns to check for malware or learn from large malware samples to classify them accordingly. By training the models on historical transaction data, deep learning can be widely used to check for fraudulent behavior in financial transactions. It can also analyze images, faces, and fingerprints which can be used for access control.
Conclusion
Artificial Intelligence and cyber security are connected. AI based solutions are indispensable as it helps strengthen the security posture against complex attacks. They can quickly detect and respond to complex threats in real time. These solutions must be adopted by any industry, considering the rise in sophisticated attacks across the globe. Traditional security solutions are ineffective against today’s threats. AI powered tools are the best bet to significantly reduce the threats. This doesn’t mean AI is the silver bullet. It must be combined with employee training programs to strengthen an organization’s security posture.
