The advancement in disruptive technologies has given rise to a plethora of opportunities for miscreants to fuel cyber sabotage and data integrity risk. The proliferation of cloud-based technologies has accelerated the process of data exchange and sharing – data is more easily accessible to users across business ecosystems. The odds for any user to enter a business ecosystem are more favorable than ever.
Organizations are struggling to deal with cyber breaches and threats on two fronts – from amateur artists who find it awarding to particular crime syndicate with intentions to destabilize the economy and disrupt the existing system. According to a report by Security Intelligence, the average cost of a data breach is $4.35 million. As per a report submitted by Cybersecurity Ventures, the cost of cybercrime will hit $8 trillion in 2023 and will grow to reach $10.5 trillion by 2025. This means the impact cyber threats will have on the world is huge. The alarming numbers will only increase if proper actionable steps are not taken.
Usher Proactive Security with Artificial Intelligence
Automated solutions such as AI and ML enable organizations to save themselves from becoming a victim of unethical practices and attempts. With AI and ML embedded in existing software technologies, organizations can ensure secured data integration and data exchange, highlight concurrent patterns in workflows, allow secured access to information, access the threat level, and send alerts to the respective team. The reason AI and ML can perform this activity is their ability to evaluate information, compare it with past actions, and infer accordingly.
The blog post discusses five ways AI-powered solutions help organizations maximize security and drive success.
1. Facilitate Database Updating and Threat Recognition
Many organizations rely on manual methods to update their databases. The risk of illegitimate access in these cases is high. Artificial intelligence and machine learning can solve this problem by providing automation capability to organizations requiring little or no human intervention to update, monitor, and manage databases.
AI and ML technologies can gather information from multiple records and catalogs, which can be further analyzed to identify potential risks of threats and breaches. What’s more, AI and ML can identify the trends of malware, virus, and spyware in an effort to map and stop unknown threats from ravaging an organization.
2. Prevent Data Breaches
Organizations have to handle large amounts of data, more than enough for any human to analyze and sift through. AI and ML-powered technologies can help them detect patterns and spot anomalies in that data much more easily and quickly.
Using AI and ML-powered solutions can also help users transform how they map and integrate data in a database. As a result, the risk around processes such as data mapping and data integration is much lower. Also, the built-in end-to-end encrypted environment allows only authenticated users to access and use data.
So, by incorporating AI mapping and integration technologies, organizations can predict deviations and handle any potential risk of data breaches with speed and efficiency.
3. Identify Uncharacteristic Action
AI helps organizations identify abnormal activities in a specific system. AI and ML-powered solutions are capable of verifying abnormal action or uncharacteristic actions by garnering a wide range of data. They do so by evaluating or scanning the system continuously. AI also minimizes unauthorised access. In case any uncharacteristic action is detected, AI leverages several factors to verify if it is in real danger or if it’s a false alarm. It uses machine learning algorithms to detect abnormal activities with precision. Hence, AI and ML can be used to point towards anything abnormal or erroneous running in the system.
4. Enable Flaws Identificatio
AI enables organizations to identify overflow of data in a respective buffer. This helps them avert risks in future. With the help of machine learning algorithms, organizations can locate errors concerning cyber security, bugs and other flaws.
Machine learning further helps AI identify suspicious information transmitted from a particular application. The malware leveraged by a miscreant to access data and systems is usually done through the vulnerabilities of computer languages.
5. Turbocharge Detection and Response Times
When an organizational network is compromised, it’s essential to take necessary measures immediately. It’s way easier and safer to detect a threat than counter it later when it has done a lot of damage. AI and machine learning can identify these threats than counter it later when it has done a lot of damage. AI and machine learning can identify these threats on a prior basis and mitigate their impact before they can cause any harm.
Conclusion
The traditional data-driven methods are not enough to manage and respond to cyber threats and breaches. The technological advancements powered by AI and ML can help organizations gain an edge and drive success without sabotaging security.
