AI can be used to help defend against increasingly advanced and disruptive malware, ransomware, and social engineering attacks on a holistic level.
FREMONT, CA: The rise of digital connectivity, combined with increasingly sophisticated cyber-threats, has emphasized the importance of smart cybersecurity. Smart Cybersecurity is a logical response to manage risk by reducing security gaps caused by dependency on manual processes, hampered by a persistent cybersecurity skills shortage and administrative burdens associated with data security management.
Despite the challenges, there is hope for decreasing human dependency and strengthening cybersecurity capabilities. A growing number of cognitive technologies will enhance safety and navigate the increasingly malicious and disruptive cyber threat environment. They are as follows:
Artificial intelligence (AI)
AI and machine learning-based computing systems are becoming more prevalent and vital in cybersecurity operations. They have become a primary focus of cybersecurity research and development in the public and private sectors. Identifying, categorizing, and synthesizing data are unquestionably beneficial in combating cybersecurity threats. AI can be used to help defend against increasingly advanced and disruptive malware, ransomware, and social engineering attacks on a holistic level. While AI is not sentient (yet), cognitive autonomy in AI has a bright future in predicting and mitigating cyber-attacks.
Machine Learning (ML)
In its most simple form, machine learning entails having a computer to function without the need for programming. It is often combined with AI, and it is sometimes referred to as the rapid acceleration of predictive analytics. ML can be used to quickly detect new cyber-attacks, draw statistical inferences, and push the data to endpoint security platforms.
Threat intelligence is one field where AI and ML will undoubtedly play a significant role in cybersecurity. It can be used to track and detect network anomalies and recognize new threats that do not have known signatures. It can also be used to connect data from different silos to understand the nature of attacks better and evaluate network vulnerabilities and risks. By cross-checking the integrity of data through multiple fragmented databases, AI and ML may aid in identity management.