EMBRACING AI IS KEY TO COMBATTING EVOLVING CYBER THREATS

INTRODUCTION:

To reduce the risk profile and potential impact of cyber threats, investments in cutting-edge security technology have increased tremendously in recent years. While this has helped close the gap with the industry's strong cyber adversaries, one of the defining issues of our day is still how to quickly identify and address cyber threats. The constant expansion of data sources, the complexity of the technological environment, and the shifting nature of the cyber threat landscape can overwhelm security personnel.

HOW AI CAN HELP?

AI has the potential to help security teams combat cyberthreats more effectively. It can speed up detection, containment, and response thanks to its capacity for adaptation, learning, and pattern recognition. It can assist in identifying and stopping attacks in real-time and automatically detecting anomalous user activity together with pattern recognition, machine learning algorithms, and predictive and behavioral analytics.

BORDERLESS ECOSYSTEM:

With the rapid adoption of new technologies, distributed setups, multi-tenant cloud environments, multiple operating environments, shared ownerships, etc., the model of a single centralized view of assets is becoming muddled, making it difficult to effectively monitor, govern, and assess the associated risk.Attack Traceability Constraints: As supply chain attacks become more prevalent and more sophisticated, large-scale, and impactful, there are growing vulnerabilities in distributed networks and end-user points for businesses, making attack traceability challenging.These elements, when combined with the severe lack of security personnel, expertise, and experience, have made matters worse for the security teams and severely limited their capacity to plan and find solutions for the future and look beyond day-to-day operations.

DATA FLUIDITY&VOLUME:

The sheer amount of data coming from various security and technology stacks, including applications, infrastructure, networks, IOT, end devices, and extended environments that are frequently the target of sophisticated attacks, is making it difficult for Security Operations Centres (SOC) to make sense of it all.

Exploding Attack Surface:

With the inclusion of new IOT devices, cloud environments, adoption of 5G, scattered workforces, and expanding partner ecosystems, the attack surface is exponentially growing, posing a severe challenge to a company's security posture.

Trust & User Behavior:

Since employees can connect using any time, location, or device, it is fundamentally unsafe and presents a number of concerns. Because user behavior and data activity have expanded in variety and unpredictability, it is now tougher to spot unusual behavior, which increases the likelihood of false positives.

Active Vulnerability Management:

AI may be used to identify and prioritize vulnerabilities in hardware and software systems, reduce the number of human, time-consuming tasks, and develop auto containment and reaction methods based on the severity. This can assist organizations in lowering their attack surface and enhancing their defenses against prospective dangers.

User Behavior Analysis:

AI is able to examine user behavior to spot shady behaviors including unauthorized access, data espionage, and insider threats. By recognising suspect URLs and email content, it can help identify and stop phishing assaults.

Automated Response:

Organizations may use AI to automate operations like setting up security policies, checking compliance, identifying threats and vulnerabilities, and responding to incidents. This enables them to respond to cyberthreats promptly and proactively. This may entail blocking.

CHALLENGES WITH THE LEGACY SOFTWARES:

Businesses utilizing older SIEM solutions or other reactive security monitoring software have access to basic log data analysis and aggregation for the purpose of identifying cyber incidents. Sadly, this can only go so far because the majority of solutions only concentrate on alert systems that will be activated if a previously established attack pattern has occurred. A legacy system frequently lacks the organization-wide visibility and scalability necessary to effectively avoid assaults, should they occur, given the constantly evolving threat landscape.The best software is available to cybercriminals, therefore even the most cutting-edge protection software can be disregarded. Due to the inability of older systems to handle the hundreds of gigabytes of data generated from numerous log sources, criminals are able to conceal their activity.

BEING PROACTIVE IN THREAT DETECTION:

Companies who refuse to update and modernize their technology and instead cling to antiquated cybersecurity methods only become less and less successful in thwarting attackers. They are merely allowing otherwise avoidable attacks to take place by depending on their ability to fix problems after the harm has already been done.Next-generation SIEM solutions can contextualize data to foresee cyber attacks rather than only identifying them at the impact stage with the correct AI system in place. To detect early warning indicators of an attack, several AI models can be utilized sequentially to maximize the threat detection output. AI-driven solutions offer near-real-time modification capability to reflect actual exposure from vulnerabilities by connecting with automated data and web scrapers to incorporate the most recent contextual threat intelligence for enterprises.

EMBRACING AI IN THREAT DETECTION IS CRUCIAL:

In order to ensure that organizations avoid the cost of potentially harmful attacks, predictive threat identification harnessing the potential of AI is essential. Companies must immediately grasp this concept if they want to guarantee that client data is kept secure and safeguarded in the face of dynamically changing dangers. Business leaders can spend more time and money on business development and less time worrying about the possibility of a damaging cyberattack thanks to AI solutions.

CONCLUSION:

While AI can't completely replace human security professionals, its capabilities for quick data analysis, real-time event processing & correlation, anomaly detection, continuous learning, and predictive intelligence can help security teams spend more time on strategic priorities and decisions while also reducing time and effort associated with responding to cyber threats.It's also crucial to remember that the traits that make AI a useful tool against security risks can also be used by adversaries to create brand-new or more advanced attacks and find system weaknesses. It can make it simpler for cybercriminals to pose as reliable users, automatically create contextual phishing and fake communications, and avoid being discovered during a compromise.To combat these AI-driven risks and cybercrime, AI-driven security tools are used, as the saying goes, "fight fire with fire."