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Artificial Intelligence in cyber security

AI in Cybersecurity
AI Cybersecurity

How does AI use in cybersecurity 2025?

AI in Cybersecurity has been among the most valuable features of the digital age. Cyberattacks have grown more advanced and frequent by the year. Consider, for example, the WannaCry ransomware attack in 2017, which paralyzed systems in over 150 countries—from healthcare to finance. Such an incident indicated the urgent requirement for more advanced, scalable, and intelligent solutions to tackle emerging cyber threats.   Those were robust systems some time ago; however, as of today, they’re just not enough to enhance threats. Hackers depend on more strategic methodologies, such as polymorphic viruses, phishing emails, and attacks using artificial intelligence. Consequently, many companies have recently considered AI a game-changer, which will increase their position in the security strength scale. AI has revolutionized cybersecurity. It allows for real-time threat detection, forecasting potential future vulnerabilities, and automatic responses so that damage can be reduced to minimal amounts. The blog looks at how AI is revolutionizing the landscape of cybersecurity, from current trends and real-world applications to challenges with integrating AI in cybersecurity systems. The Cybersecurity Landscape Today Changing Nature of Threats Cyber threats today are more complex and vicious than ever:   Ransomware: Ransomware attacks have now become a multi-billion-dollar criminal business. The 2021 Colonial Pipeline attack stopped fuel supplies in the U.S., forcing the company to pay millions in ransom.   Phishing and Social Engineering: Cybercrimes use phishing emails and impersonation tactics to hoodwink people into revealing confidential information. Recently, in 2020, audio was used to impersonate the CEO of a company to deceive the company into transferring $243,000 to fraudsters.   Polymorphic Malware: is malware that constantly changes its code. Thus, it evades detection and can’t be easily caught by traditional signature-based systems. Limitations of Conventional Security Control Traditional security control is well at work in the fight against known threats but has become inefficient against the complexity and volume of modern threats:    Static Defense Mechanisms: A signature-based system leaves out new or changing threats, such as zero-day vulnerability.   Processes Involving Manual Monitoring: Humans struggle to keep track of the vast number of alerts, causing alert fatigue and missed detection.    Scalability Challenge: Digital infrastructures are growing, thus more advanced systems are needed to secure large-scale networks.   These constraints highlight the importance of AI-based solutions, which can learn, adapt, and respond to threats in real-time. How AI is Revolutionizing Cybersecurity: Threat Detection and Prediction AI can detect threats by analyzing large volumes of data and establishing patterns and anomalies.ML models can predict attacks based on past behavior.   Anomaly Detection: AI-based systems create a baseline of normal behavior and raise alerts in real-time when something deviates from the norm.   Predictive Analytics: Using AI, strength and attack trends of vulnerabilities are analyzed in advance to take proactive measures, such as patching vulnerabilities before exploitation. Malware Detection and Zero-Day Threats Malware detection is enhanced through behavior rather than code signatures, making it highly effective against polymorphic malware and zero-day threats.   Behavioral Analysis: It evaluates files’ behavior in isolated environments and identifies malicious intent without pre-existing signatures.   Real-Time Response: AI-driven systems can instantly detect and neutralize threats, preventing damage from spreading across networks. Securing Networks AI strengthens network security through automated monitoring, intrusion detection, and response systems.   AI-Driven Intrusion Prevention Systems (IPS): These systems analyze network traffic, identifying and mitigating threats before they cause harm.   Autonomous Isolation: Compromised devices can be automatically quarantined to prevent further breaches. Social Engineering Mitigation Social engineering attacks like phishing are among the most difficult to detect. AI uses advanced tools to fight these threats:   Natural Language Processing (NLP): AI in data security analyzes email content to detect phishing attempts, flagging suspicious language or sender behavior.   Deepfake Detection: AI algorithms identify inconsistencies in video and audio files, mitigating the risk of impersonation. Real-World Applications of AI in Cybersecurity AI-Powered Tools in Use Several tools illustrate the strength of AI cybersecurity:   Darktrace: Leverages unsupervised learning to understand network behavior and detect deviations.   CrowdStrike Falcon: Provides the amalgamation of endpoint protection along with real-time threat intelligence to further enhance security.   Cynet 360: Offers an integrated platform to protect endpoint, user, and network systems. Industry-Specific Use Cases Healthcare: AI and cybersecurity protect patient records against ransomware attacks while complying with data protection regulations.   Finance: Fraud detection systems use AI to monitor any transactions containing defects and bring them to the system before escalation occurs.   Energy: AI protects critical infrastructures like power grids and oil pipes against cyber attacks. Case Studies Stopping Ransomware in Healthcare: A US-based hospital’s use of AI-based tools detected unusual encryption activity, and thus, the ransomware attack was checked at its earliest stage.   Preventing Phishing Scams: An AI-based email security system helped a financial firm reduce phishing-related incidents by 85%. Emerging Trends in AI-Powered Cybersecurity Federated Learning for Privacy-Sensitive Threat Detection Federated learning allows organizations to collectively train AI models without sharing sensitive data. This decentralized approach enhances AI information security without violating data privacy. Quantum-Resistant Security Quantum computing has become a significant threat to encryption methods. AI significantly contributes to developing quantum-resistant algorithms and securing data in the post-quantum world. Biometric Authentication AI improves authentication systems by integrating multiple biometrics, including facial recognition, fingerprint scanning, and behavioural analysis.   Example: Banks and financial institutions are using voice recognition and AI-driven behavioral analytics to verify and authenticate customers with security and fluidity. AI-Driven Automation in Incident Response The SOAR solution uses AI to prioritize and automate responses to threats and reduces downtime as well as the associated operational costs significantly. AI with Blockchain The combination of AI with blockchain is advantageous because it provides increased transparency and security. The blockchain logs are tamper-proof, and AI can scan the data for any abnormalities. In this manner, an effective defense mechanism is obtained. Latest Penetration Testing Report Download Challenges and Limitations of AI in Cybersecurity AI has revolutionized the cybersecurity landscape. However, this technology has challenges and limitations. Understanding these concerns is essential to balancing the

AI-Powered Threat Intelligence_ Enhancing Penetration Testing Strategies
Penetration Testing

AI-Powered Threat Intelligence: Enhancing Penetration Testing Strategies

When we discuss proactively testing our environment or applications to look for vulnerabilities ahead of a hacker, we talk about penetration testing or “ethical hacking” exercises. This concept is quite old. When you’re trying to find deficiencies in your processes and controls through simulations or cyber attacks, then you are performing a penetration test and this entails hiring a penetration testing company. By incorporating AI Threat Intelligence, you can further strengthen your defenses by identifying emerging threats in real time. Evolution of Pen testing The penetration testing practice has evolved with time from an entirely manual and burdensome process of which only a few people knew the art to now being a rather automated and much-propagated process. This goes hand in hand with the evolution of technology.  In the early days, most processes were done with a lot of computers, so it was quite efficient to conduct manual penetration testing. Later on, as computers multiplied and processes began to get automated, penetration testers were forced to automate their tools in order to cover more ground in a shorter period of time, thus faster detection of vulnerabilities. Now, we have reached a point where companies possess different types of technologies and hundreds of thousands of IP addresses. Therefore, it becomes more challenging for pen testers to check everything within a reasonable amount of time with precise results. That is why the use of artificial intelligence and machine learning has started to help pen testers get past these barriers. Artificial intelligence is described as the ability of a machine to perform tasks that simulate human intelligence. A subset of artificial intelligence is machine learning, referring to the concept that a system can learn and adapt without following specific instructions but as an alternative through algorithms and statistical models studying statistics to draw conclusions. Related Read: Impacts of AI on Cybersecurity Challenges with Traditional Penetration Testing Even though pen testing is a crucial part of cybersecurity, the traditional methods are often highly challenged in the following ways: Explore: AI-Based Application Penetration Testing and Its Importance Is AI Used in Penetration Tests? So just how can AI and ML support penetration testing? Let’s take a look and analyze the different phases in a normal penetration test assessment and determine where AI and ML can be used. There are several well-known methodologies and standards that can be used to perform penetration tests such as OSSTMM (Open Source Security Testing Methodology Manual), OWASP (Open Web Application Security Project), NIST (National Institute of Standards and Technology), PTES (Penetration Testing Methodologies and Standards), ISSAF (Information System Security Assessment Framework). But for a better-streamlined analysis, we will only mention the four stages of penetration testing in which Artificial Intelligence and Machine Learning will be applied:  1. Information Gathering and Reconnaissance – Through this phase of pen testing, we try and gather as much information as possible about our targeted system by bringing information from easily accessible sources into light to derive the open ports and services during this phase. At the end of this phase, we would have a dossier of our targets including information such as domain names, target hosts, services enabled, technologies in place, employees’ names, employees’ emails, physical locations, pictures of the physical locations, potential usernames and passwords, etc. 2. Vulnerability Assessment / Scanning – In this penetration testing phase, we do more in-depth vulnerability scans trying to determine all the potential vulnerabilities that the targets could have. Here, AI and ML could aid the pen tester in understanding what the scans report by analyzing and filtering out whatever is not relevant or produces noise, considering all the information extracted from the first phase combined with threat intelligence drawn from social media, open records, the deep web, dark web, etc. This will also enable AI and ML to determine the best course of action for the attack phase by correlating all gathered information and knowledge. 3. Exploitation – This is the phase of pen testing where we put into action everything that was planned before. Here, we try, among other things, to gain access to the systems, perform lateral movements, escalate privileges, gather more information, and maintain persistent access. As I mentioned previously, AI and ML can support by determining what is the best possible course of action to penetrate the target, and they can carry out the exploitation simultaneously. Their results can feed back into the AI model such that it creates exploitation alternatives or new exploitation pathways not considered up to this time. 4. Reporting – At the end of this stage, a comprehensive report inclusive of all details regarding the issues discovered, the implications of these risks, and recommendations are provided to the penetration testing client. AI and ML can bolster the reporting by processing the data that has been gathered during the assessment and linking them to threat intelligence and knowledge obtained in previous engagements to produce actionable insights applicable to the organization undergoing review. AI-Driven Tools for Penetration Testing Several AI tools are being developed to accelerate penetration testing: These tools assist ethical hackers in uncovering vulnerabilities faster and more accurately, improving the overall security of the systems.  Advantages of AI-Enhanced Penetration Testing AI brings with it a host of benefits for the penetration testing process: AI makes the penetration testing process significantly faster as it automates all repetitive tasks such as scanning for vulnerabilities. The Future of AI in Penetration Testing As AI continues growing, so does its scope of work in penetration testing. AI futures may involve the autonomous generation of test cases, predicting new cyber attack techniques, and continuously improving the ability to detect existing ones. Along with these factors, the expertise of human professionals and AI together will continue to protect people from emerging threats in the realm of cyber attacks. Also Read: The Evolution of Penetration Testing: From Manual to AI-Driven Approaches Why Do Pen Testing Certifications Matter?  There are several penetration testing certifications that have been recognized. Most require previous experience in systems administration

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Pabitra Kumar Sahoo

Pabitra Kumar Sahoo

COO & Cybersecurity Expert

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Pabitra Kumar Sahoo

Pabitra Kumar Sahoo

COO & Cybersecurity Expert