Today’s AI plays an intrinsic role in an effective cybersecurity strategy
Security continues to evolve through a complex ecosystem of technologies, including intrusion detection and prevention systems (IDS/IPS), security information and event management (SIEM), log management, data loss prevention (DLP), firewall monitoring, and advanced threat detection tools.
All collect data that could help you prevent a breach – but the sheer volume of security data has traditionally been overwhelming, especially when analyzing sensitive information manually. How do you quickly determine what’s noise and what’s a warning bell? Enter artificial intelligence (AI) and advanced automation, which are quickly becoming game-changers in managing and mitigating cybersecurity risks.
AI and robotic process automation (RPA) have transformed from promising technologies to critical components of enterprise strategies. They can speed up security data evaluation, spot vulnerabilities before they become breaches, and free up time for your security practitioners to address threats more proactively.
Here are four key ways AI is reshaping endpoint security and how you can leverage today to transform your cyber strategy.
1. Advanced data intelligence
The traditional process of consolidating all your data into a single repository and then analyzing and understanding its implications can be challenging, inefficient, and, most importantly, time-consuming.
It’s time to move on from the old ways. Today, AI doesn’t just collect and aggregate data – it provides contextual, predictive insights.
Machine learning models can now process petabytes of security data in near real-time and forecast potential security incidents before they occur. AI models streamline the data collection process, from identifying complex, multi-stage threat patterns to creating dynamic, interactive threat visualization dashboards.
Instead of having your team perform predictive analysis, proactive threat monitoring, and threat hunting manually, they can move directly into action mode to secure weak points.
2. Hyper-automated vulnerability assessment and management
Modern vulnerability assessments have become significantly more sophisticated, largely driven by AI.
Machine learning models can simulate millions of potential exploit scenarios. Integration with threat intelligence platforms provides global, real-time context about emerging vulnerabilities, and predictive AI can now accurately estimate the potential business impact.
In addition, you can use AI and RPA to create a model that predicts how a vulnerability will behave. Using data from your SIEM tool, AI can examine what malware or endpoint security issues may have been detected. Based on that data, RPA can put security controls in place.
Security professionals can even use AI to analyze malware patterns and behaviors, and predict how it will impact your endpoints. You can use this information to deploy a more accurate countermeasure without the potentially false positives non-AI solutions often provide.
3. Intelligent data loss prevention (DLP)
AI can protect your organization’s sensitive data by identifying what information should be secured, using advanced natural language processing.
Using vector machine learning (ML), you can understand, identify, and make sense of certain patterns. For example, by learning the data patterns of your organization’s financial data, AI can strip that information – or encrypt it – if someone accidentally sends the data to unauthorized personnel or if a malicious attacker tries to gain access.
Today’s AI goes beyond traditional pattern matching with enhanced behavioral anomaly detection, context-aware encryption and data masking, and real-time risk scoring for data transmission and access attempts.
4. Secure software development
AI helps organizations prioritize security from the very beginning of software development, playing a critical role in its lifecycle.
AI can work within the agile development process, sifting through code as it’s developed and running it against the latest security threats and trends to spot any potential flaws or vulnerabilities. If there’s a strong probability the code may have flaws, AI will alert the developers to fix vulnerabilities at each phase of development.
Using AI and RPA helps developers proactively identify issues with predictive vulnerability detection during the design and development phases, and continuous code security scanning can suggest immediate remediation. You can also integrate security testing directly into continuous integration and continuous deployment (CI/CD) pipelines.
Contact your dedicated Account Manager:
Kyle Thomas 
SM SLED Manager
732-564-8526
Kyle_Thomas@shi.com