A Comprehensive Guide to Implementing Artificial Intelligence Security Solutions

A Comprehensive Guide to Implementing Artificial Intelligence Security Solutions Table of Contents Facebook Instagram Twitter Linkedin-in As cybercriminals are evolving their techniques constantly to breach security systems, the need for artificial intelligence in this domain has become indispensable for companies using traditional security solutions. Notably, artificial intelligence security solutions offer an advanced toolset to businesses to deal with the ever-persistent threat of cyberattacks. Featuring an intelligent defence mechanism against malicious attacks, AI-powered security solutions specialise in proactive threat hunting and identifying suspicious patterns. Read ahead to learn more about AI basics and applications of artificial intelligence in cybersecurity. Basics of Artificial Intelligence Involving technologies that understand, learn and act on the basis of acquired and derived information, artificial intelligence works in three ways: Assisted intelligence: Improves what people are already doing Augmented intelligence: Enables people to do things they couldn’t do otherwise Autonomous intelligence: Develops machines to act on their own in the future Further, it is to be noted that machine learning, neural networks, deep learning and expert systems are subsets of AI technology. Known for their iterative and dynamic nature, artificial intelligence systems not only learn from their experience, but also get smarter with the more data they analyse. Also, they get increasingly capable and autonomous as they go. Having some degree of human intelligence, AI systems also mimic how humans think, solve problems and make decisions using rules-based reasoning. How is AI Used in Security? As more and more people are adopting AI systems for decision-making and data processing, protecting their integrity and maintaining their confidentiality are crucial. Adopting a zero-trust approach, artificial intelligence security systems can not only safeguard insights but also compartmentalise operations in order to reduce attack risk. From data collection to model training, these solutions secure the entire AI lifecycle to protect the process of generating insights. Also, they enhance security in AI processes by using segmentation that involves separating and restricting AI workflow phases. Further, AI security systems follow the no implicit trust strategy. This is even applicable to internal users and systems. Notably, AI systems can be assessed for trustworthiness, authorisation and authentication, extensively. Further, artificial intelligence is used increasingly to enhance threat detection, incident response, and overall cybersecurity. It is to be noted that AI security can be improved by: Using machine learning for anomaly detection Adopting behavioural analytics to detect variations Automating model deployment in secure containers Real-time monitoring to identify new threats and vulnerabilities Signatureless detection Detecting and preventing network intrusion Optimising firewall rules and identifying vulnerabilities Enhancing endpoint protection Detecting and preventing malware infections Identifying unknown threats Enabling secure access using biometrics Behaviour-based authentication Optimising security operations SIEM enhancement Chatbots and virtual assistants help in real-time incident identification and mitigation Analysing e-mail content Detecting phishing and fraud Highlighting suspicious financial transactions Automated vulnerability scanning and assessment Evaluating vulnerability severity Security automation and orchestration Analysing threat intelligence feeds Identifying suspicious activities for improved physical security Automating compliance checks Generating reports to meet regulations Analysing static and dynamic code for security vulnerabilities during development For security teams, sophisticated cyber attackers, an explosion of data and growing infrastructure complexity pose difficult challenges. Also, these challenges hinder their ability to safeguard data and manage user access, as well as quickly detect and respond to security threats. But, artificial intelligence technologies, including machine learning, have the potential to deal with these issues more efficiently than traditional software. Self-learning, AI-based security systems impart a new level of intelligence to humans. These solutions offer complete, accurate information related to IT asset inventory with access to information systems. Also, AI-based security systems can provide the latest updates on global and industry-specific threats to help make critical prioritisation decisions. Artificial intelligence security solutions help identify the strengths and gaps in AI systems. They can also predict how and where the system is most likely to be breached. Prescriptive insights must be paid attention to for enhancing controls and processes to effectively improve cyber resilience. Additionally, AI-powered security systems can offer improved context for prioritisation and response to security alerts. Benefits of Using AI in Security The following are the advantages of using artificial intelligence to enhance cybersecurity: It is vital to protect data across hybrid cloud environments. AI-powered security solutions can not only identify shadow data and monitor abnormalities in data access, but also share alerts pertaining to potential threats by anyone accessing the data or sensitive information. This helps save valuable time in identifying issues and offering relevant solutions in real time. Risk analysis conducted using AI can produce incident summaries and automate incident responses. This helps accelerate alert investigations, identify vulnerabilities and defend against cybercriminals. AI security models help strike a balance between security and user experience. They achieve this by analysing the risk of each login attempt and verifying users through behavioural data. AI-powered security systems help maintain a high security posture by preventing phishing, malware and other malicious activities. Early AI Adopters The following are some early adopters of artificial intelligence security solutions: IBM: Using machine learning, Watson’s cognitive learning platform helped the IBM team in knowledge consolidation tasks and threat detection. Google: Gmail has been using machine learning to filter e-mails since its launch 18 years ago. Today, most of its services use AI technologies, including deep learning that allows algorithms to make more independent adjustments as they train and evolve. Popular Artificial Intelligence Security Solutions In order to detect new types of attacks, AI-powered security solutions analyse a huge quantity of risk data and the relationship between threats in enterprise information systems. This can further help reduce breach risk and enhance security. When talking about the applications of artificial intelligence in security, it is important to discuss the following top-rated AI-powered solutions: Serial Number Security Software Speciality Key Highlights Limitations 1. Darktrace Handles new threats Empowers security teams with autonomous, AI-driven capabilities Bolsters defences Offers instant visibility of cyberattacks Provides responsive and quality technical support It has a complex initial setup Many users, including small businesses, consider it pricey 2. SentinelOne Provides round-the-clock threat hunting and managed services Enables companies to ingest data from various sources Its reporting capability can be improved Digital forensics and incident response are only available to enterprise users Its reporting capability can be improved Digital forensics and incident response are only available to enterprise users 3. CrowdStrike Monitoring user endpoint behaviour helps stop identity-based attacks in real-time The platform is highly modular and extensible Utilises an AI-based detection system called user and entity behaviour analytics Offers pre- and post-incident response services Provides visibility across devices, users, accounts, applications, and cloud workloads Gives Real-time insights with automated threat intelligence Requires time investment for users to get familiar with the platform’s capabilities User interface can be improved 4. Fortinet Preventing zero-day threats Security profile customisation Protects against network- and file-based threats AI-based inline malware prevention AI-powered SandBox engine Customer support can be better Steep learning curve 5. Zscaler Data loss prevention Advanced data classification AI-powered browser isolation Inline web protection AI-powered browser isolation AI-powered phishing and C2 detection Standard digital experience monitoring Automated, AI-powered root cause analysis Needs fast internet service to function well Customer support can be improved 6. Check Point Software Network monitoring and security Improves accuracy with network AI and mobile AI engines aggregator as well as machine-validated signature Exposes stealth breaches Prevents known and zero-day attacks across networks, endpoints, cloud, and e-mail Identifies anomalous behaviour indicative of a potential threat Anti-phishing AI engine ThreatCloud AI campaign hunting Some users may find it expensive Steep learning curve 7. Trellix Meant for Complex IT environments that require continuous monitoring Integrates with 1,000+ third-party sources Signature-based engine to find and block known malware Comprehensive on-premises product portfolio Behaviour-based analytics engine to stop advanced threats Real-time monitoring Complex initial setup User interface can be improved 8. Cybereason Defending against MalOps Quality customer support Automatic prediction and response to mitigate attack without human intervention Easy deployment Posture and incident management Threat intelligence Mobile threat defence Lacks reporting capability Steep learning curve 9. Vectra AI Hybrid attack detection, investigation, and response AI-driven insights Attack prioritisation Detects attacks in real time with behaviour-based threat detection Automatically analyses attacker behaviour Automatically correlates, scores, and ranks incidents by urgency Product documentation can be improved Reporting capability can be improved 10. Tessian Protection against e-mail-based threats Threat network analysis Hunts, investigates and responds to e-mail threats quickly E-mail exfiltration protection Protects against QR code phishing, BEC, executive impersonation, and domain spoofing Behavioural analysis Automates removal of end user-reported threats Some users reported occasional false positives Steep learning curve Conclusion By and large, artificial intelligence-powered solutions offer the required support to adequately protect the dynamic enterprise attack surface. They help improve the speed, accuracy and productivity of security teams. These transformative AI security solutions accelerate threat detection, expedite responses and protect user identity and datasets. Additionally, they reduce breach risk and improve security posture by identifying and prioritising risk, instantly spotting any malware, detecting intrusions before they begin and guiding incident response. If you want to explore these powerful human-machine partnerships that drive cybersecurity, then connect with Jaro Education to enroll in the Executive Programme in Artificial Intelligence and Cyber Security for Organizations [EPAI&CSO by IIM Indore. Frequently Asked Questions Will artificial intelligence take over cybersecurity? Considering the ever-evolving nature of cyberattacks and the proliferation of devices, cybersecurity cannot be maintained by using traditional security systems alone. AI-based tools are playing a vital role in reducing breach risk by providing real-time monitoring of the attack surface. Also, artificial intelligence security solutions improve security threat detection and prompt instant action. Not just this, they have gained the capabilities to predict cyberattacks with matchless precision. Does AI improve security? Artificial intelligence is a self-learning technology that automatically and continuously improves itself. Augmenting human expertise, AI solutions not just quickly analyse millions of events, but also identify different types of threats at the same time. Results of AI analysis are used to derive a correlation amongst patterns that are relevant to the enterprise attack surface. Additionally, AI security solutions streamline incident response and mitigation processes. Are there other artificial intelligence security solutions? Artificial intelligence is a self-learning technology that automatically and continuously improves itself. Augmenting human expertise, AI solutions not just quickly analyse millions of events, but also identify different types of threats at the same time. Results of AI analysis are used to derive a correlation amongst patterns that are relevant to the enterprise attack surface. Additionally, AI security solutions streamline incident response and mitigation processes. What are the highlights of EPAI&CSO? The 10-month programme is designed to help non-tech professionals, engineers and IT managers gain insights into big data, artificial intelligence, machine learning, and cybersecurity through multi-disciplinary training. It helps acquire practical skills in risk management, compliance, incident response and leadership. What are the eligibility requirements for enrolment in EPAI&CSO? Any graduate from a recognised Indian university with a minimum of 50% marks or equivalent and at least two years of work experience can get admission in IIM Indore’s EPAI&CSO course. 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