Artificial Intelligence and Cybersecurity – Addressing the Threats and Challenges

AI and cybersecurity are two rapidly evolving fields that are entwined in a complex relationship, as artificial intelligence(AI) brings both advancements and vulnerabilities to the field. Attackers are also using AI for sophisticated attacks,e.g., using generative AI to conduct social engineering attacks such as crafting phishing emails, developing intelligent malware that adapts rapidly to the user environment, amplifying traditional attacks like DDoS (distributed denial of services), and evading cybersecurity defenses.

AI can empower threat and vulnerability detection

Anomaly Detection: Algorithms can analyze vast amounts of network traffic, user behavior, and system data to flag unusual patterns that may indicate a cyberattack. It can identify fraudulent transactions, indicators of compromise, ransomware/malware at the point of entry, and detect crypto mining malware attacks that siphon off precious computing resources. It can also identify sensitive information leakage like PII and IP. AI can identify known malware and attack patterns by matching against a database of known threats.

Entity Behavioral Analytics: AI can uniquely fingerprint each user account, machine activity, and network chatter, and using unsupervised learning detect pattern shifts and flag threats as they arise. It can detect various social engineering attacks like phishing and spam.

Threat Intelligence and vulnerability scanning: AI can process and analyze vast amounts of threat intelligence data to provide real-time information on emerging threats. It can proactively discover vulnerabilities in a network or system by analyzing historical data on past breaches. It can also be used for risk assessment, generating incident summaries, enhancing telemetry, and designing incident response and mitigation plans. AI can facilitate threat intelligence sharing among partners to improve the collective security posture.

Cyber-security testing and threat hunting: AI can be used in advanced penetration testing to detect gaps. It can simulate hundreds of known attacks and test the resiliency of enterprise defenses.

AI can augment response and prevention

AI-powered Intrusion Detection Systems (IDS) Intrusion Prevention Systems (IPS) and Security Information and Event Management (SIEM): AI-powered cybersecurity systems can automate responses to threats in real-time.  This includes isolating compromised devices, blocking suspicious network traffic, and repairing corrupted systems. By embedding intelligent automation, user-behavior analytics, and real-time data analysis for threat detection into firewalls, it has become possible for companies to reduce response times drastically. AI models continuously learn using reinforcement learning and adapt to the changing threat landscape. It can ensure business continuity by automatically provisioning standby resources and diverting traffic to them.

Empowering Security Analysts: AI can transform the activities of the SOC (Security Operations Center), by automating response to tedious L1 and L2 alerts. Security analysts are then freed up to tackle the more challenging security problems. AI can empower patch management to prioritize the vulnerabilities that must be patched and automate the process resulting in better workload management. It can automate backup storage without human intervention, and aid in recovery and disaster management activities.

User authentication

AI can prevent unauthorized access to systems or data. Biometric authentication, facial recognition, and fingerprint scanning can be strengthened using AI to improve security without compromising user experience. Incorporating AI into Multi-Factor Authentication (adaptive MFA) enables the system to verify identity by validating a user’s digital fingerprint with a library of recognized user behavior patterns.

Enforcing Zero Trust security: AI can be used to implement a Zero Trust security model by continuously verifying the identity and security posture of all devices and users attempting to access resources. AI-powered network security and segmentation can be very effective when dealing with a large number of networks, devices, and users, enforcing security policies at scale, and mitigating third-party risks when enterprise systems are interacting with external systems can protect cloud environments, safeguarding IOT devices by monitoring configurations, detecting misconfigurations, and ensuring compliance with security policies. It can aid in advanced encryption and cryptography like homo-morphic encryption, anonymization, and differential privacy thus protecting sensitive data.

Also, Enterprise AI has a host of different vulnerabilities. One of the most overlooked aspects of cyber-security is the enterprise AI systems in production, which are susceptible to adversarial attacks. Attackers can manipulate the behavior of AI models by poisoning their training data, extracting information from the AI model on its training data by administering controlled inputs, nullifying the work done by the model by exploiting its vulnerabilities, and causing harm in many other ways. In LLMs, attackers can conduct prompt injection attacks that cause the model to negate all user prompts and behave as the attacker wants it to. Securing AI systems requires a holistic approach that considers both traditional cybersecurity measures and AI-specific safeguards.

Road ahead

To build these state-of-the-art AI defenses, the basic foundations of enterprise security comprising of people, processes, and technology must be mature enough. Organizations must conduct a thorough discovery exercise internally and rethink their entire security outlook in the context of AI.

Authored By: Balakrishna D.R., Executive Vice President – Global Head of AI and Industry Verticals, Infosys