BFSI industries face critical cybersecurity challenges that need critical resources to address the problem. Banking, financial services, and insurance (BFSI) face cybersecurity risk due to continuous digital transformation. The implementation of artificial intelligence in cybersecurity risk management plays an important role in anomaly detection, automated incident response, fraud prevention, and many other areas. Cybersecurity maintains the integrity of the financial system. The BFSI industries need to handle vast amounts of financial data and sensitive information, and they need managed security services.
Major cyber threats in the BFSI sector
Cyber criminals are using more sophisticated tools to breach the security of entire systems, and this makes BFSI industries more vulnerable to potential risk.
- Phishing is one of the prevalent cyber threats where cybercriminals steal sensitive information like account details, passwords, and many other relevant information. Unauthorized access results in financial fraud and phishing.
- Ransomware is a malware attack that uses sophisticated technology to encrypt the user’s information and disrupt daily operations. It uses emails with vulnerable risk and accesses critical systems.
- Insider threat is a potential risk in BFSI sectors because the unintentional mistake of employees, vendors, or any user gives unauthorized access to sensitive information.
- Advanced persistent threats (APTs) are a severe cybersecurity risk faced by financial institutions, where attackers infiltrate the entire network and stay within the system for an extended period of time. It gets unauthorized access to sensitive financial data, user information, intellectual property rights, and any other relevant information.
Real-time threat detection and managed security services
The artificial intelligence and machine learning applications provide an advanced method for detecting and mitigating the potential cyber risk associated with BFSI enterprises.
- Predictive intelligence for threat detection
Threat detection using predictive analytics analyzes the data from various sources to identify the vulnerable risks and generates warning signs well in advance to take necessary actions before the cyberattack.
- Behavior analytics
It uses machine learning to identify the user behavior pattern and spot the anomalies in advance to prevent potential financial fraud.
- Automated security operations
Security Orchestration, Automation, and Response (SOAR) enables enhanced security operations by automating every operation and maintaining a smooth workflow throughout the business processes.
- Artificial intelligence-driven Network security
The advanced intrusion detection system (IDS) improves cybersecurity by accessing real-time data. It analyzes the network traffic and detects threats on time, setting alerts throughout the system.
- Biometric security and multifactor authentication
Multifactor authentication and biometrics require multiple authentications from the user before giving access to the system. The enhanced secure authentication system provides a secure platform, preventing any unauthorized access to sensitive financial information.
The BFSI sectors are adopting emerging technologies with their traditional systems to mitigate the risk that can break down their systems. Artificial intelligence and machine learning have been integrated to provide an added layer in cybersecurity. The predictive and behavioral analytics give assurance of a fraud detection and prevention strategy in the BFSI sector. Digital threats are targeting the BFSI sectors, but proactive cybersecurity measures have been adopted by various enterprises already to deal with vulnerabilities