Today, cybercriminals are using innovative technology tools to carry out deadlier attacks. As cybercrime becomes increasingly impactful, security needs to keep up.
As the pace of technology innovation picks up, security attacks also become more deadly. Cybercrime is also adopting newer technologies for higher success in these attacks.
It’s time for enterprises to use smarter technology and tools to fight attacks and stay safe.
AI is one of the technologies that is continuously evolving. It could be the smartest choice for companies that wish to fight innovative cyber-attacks and stay secure.
AI is taking center stage as one of the most significant technological innovations. Today, it has a huge role to play in security as well. Artificial intelligence has several advantages over other technologies.
- First and foremost, it creates the base of many other new technologies. For example, AI tools can help deploy automation. All applications can work better, faster, and smarter with smarter machine learning and automation.
- Artificial intelligence (AI) tools can work with Large Language Models and databases to pre-empt and predict attacks. Security applications need the power of data to fight increasingly innovative cybercrime.
- Also, as businesses become increasingly digital, the importance of AI in cybersecurity increases. Unprotected systems are now exposed to breaches through simple actions like connecting to public Wi-Fi.
According to Statista’s Cybercrime Encounter Rate in Selected Countries 2022 report 2022, around four in ten internet users worldwide have experienced cybercrime.
Key Features that Make AI the Best Choice:
- Machine Learning (ML): Harnessing algorithms to analyze data and make predictions based on historical patterns.
- Cybersecurity Automation: Setting security protocols to counter cyberattacks and protect sensitive data.
- Vulnerability Management: Assessing and enhancing existing security policies and protocols.
- Automation of Authentications: Enhancing security with AI-driven identification tools.
- Accurate Risk Assessments and Threat Intelligence: Offering automated suggestions for robust security protocols.
- Threat Detection: Utilizing AI and ML to spot unusual behavior. Also, to detect system variations indicating potential security breaches.
- Anomaly Detection: Analyzing patterns to detect variations and anomalies in network traffic.
Security remains important in both the physical and digital landscape. AI-powered cybersecurity software fights risks. It prevents unauthorized data access, attacks, identity theft, and human errors.
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Here are four noteworthy AI security tools widely adopted in the industry:
Crowdstrike operates the Crowdstrike Falcon security system. It utilizes an AI-driven detection system called User and Entity Behavior Analytics (UEBA) and zero-day attack mechanisms.
It tracks all endpoint activity user behavior and monitors system operations. This helps to build baselines for consistent activity. The system logs every process and triggers alerts for deviations in user actions.
Also, it warns of unseen system processes activating additional tracking methods.
- Cloud-native Next Generation AntiVirus (NGAV) protects many attacks. It fights common malware and complex threats without requiring signatures or a costly framework.
- Endpoint Detection and Response (EDR) monitors and captures endpoint activity. It delivers in-depth analysis to detect and mitigate potential threats.
- Forensics streamlines the collection of point-in-time and historical forensic data. It helps in robust cybersecurity incident analysis and periodic compromise assessments.
- Firewall Management enforces firewall policies through a centralized approach. It ensures rapid deployment, operation, and defense against network threats.
This enhances protection and informed actions.
2. Cognito by Vectra
Vectra’s Cognito, an AI-powered platform, detects and responds to cloud-based cyberattacks within minutes. Its automated threat detection system defends against stealthy attackers.
Cognito detects, analyzes, and investigates data and systems in real time. This empowers security teams to identify early-stage attacks and safeguard against malicious activities.
- Threat Detection and Response utilizes attack signal intelligence. This identifies and prioritizes unknown threats across Cloud, SaaS, Identity, and other networks.
- Attack Signal Intelligence generates AI-driven detections and triages.
- AI-driven Detections analyze signatures and anomalies. It detects attacker behavior and narrows attacker TTPs down the cyber-kill chain.
- AI-driven Triage examines patterns specific to a data system’s environment. This exposes significant events and filters out noise.
- AI-driven Prioritization enables analysts to focus on critical risks. This reduces business risk by providing insights into threats’ severity and impact.
- Managed Detection and Response (MDR) Services offer complete visibility and collaboration for assessing and resolving threats.
3. IBM QRadar Advisor with Watson
IBM’s QRadar Advisor accelerates threat remediation. QRadar SIEM prioritizes high-fidelity alerts to capture stealthy attackers. It prevents significant threats and vulnerabilities from disrupting business operations.
- This allows them to confirm threats and identify potential attack methods using confidence levels at each stage.
- The analyst learning loop evaluates systems and enhances the escalation process.
- Conducts cross-investigation analytics by linking investigations through connected incidents. This reduces duplication of effort and streamlines efforts.
- Tunes the system to reduce duplicate investigations triggered by the same events, enhancing security.
DefPloreX is an AI-driven ML toolkit designed for large-scale cybercrime forensics. This security software analyzes metadata records within files and utilizes headless browsers to access resources.
It extracts data from defaced web pages and stores results in an Elastic index. Elastic Search allows for easy data integration into other systems.
- ML and visualization techniques convert unstructured data into meaningful descriptions.
- Utilizes various Python-based data analysis techniques and tools. This creates offline “views” of data.
- Segments similar defaced pages into clusters and organizes web incidents into campaigns for efficient data analysis. The clustering technique is parallel and not memory-bound.
- Offers text and web-based UIs that are easily queried using a simple language for investigations and forensics.
How AI Enhances Security
AI is certainly a crucial asset in modern cybersecurity. It offers threat intelligence for identifying and analyzing cyber-threats. It secures networks, websites, and services against malware, ransomware, and other cybercrime.
With its almost unlimited capacity for data analysis, it can predict cyber-vulnerabilities and attacks. Forewarned is forearmed. So, it can help enterprises reduce the attack’s impact or eliminate the vulnerability.
This is the strength of AI for cyber-security.