What is AI Security?

Understanding AI’s Role in Modern Cybersecurity

Traditional cybersecurity systems concentrated on preserving a network’s or device’s operational state.

Modern threats, however, rarely seek to cause outages, but instead aim to:

• Steal valuable corporate data
• Deploy complex strains of malware behind perimeter defenses

To bring defenses in line with these goals, security staff are required to monitor more pieces of data.

The Rise of SIEM & EDR

This changing goal can be tracked in the security tools that have become popular over time – the rise of Security Information and Event Management (SIEM) tools in the early 2010s saw a push toward ingesting and analyzing large quantities of log files.

Since then, the amount of data being ingested has increased.

Endpoint Detection and Response (EDR), for instance, continuously monitors the internal activities of every company laptop, phone, and PC, while firewalls do the same for network-level activity. These individual pieces of data are created faster than can be manually investigated.

(but they still need to be turned into actionable intel.)

This is where AI, such as Machine Learning, has made significant strides.

The Adoption of AI

It works by training algorithms on large datasets, from which it organizes network or malware data into recognizable patterns. These patterns can then be applied across new sets of data, allowing anomalies to be recognized automatically, such as:

• Unusual login attempts
• Data access patterns

Over time, the ML models adapt and improve their accuracy by continuously learning from new data.

It’s just one way in which AI allows human cybersecurity teams to work faster, more efficiently, and assess wider swathes of threat intelligence than is possible with just human eyes.

AI Security Risks

While AI has significant promise and potential benefits in numerous industries, it can also introduce security risks, including the following:

• Data Breaches: AI models require large volumes of data for training. Collecting and using these large data sets introduces the potential risk that they will be breached by an attacker.
• Adversarial Attacks: The integration of AI into various processes introduces the risk that cyber attackers will target the AI. For example, attackers may attempt to corrupt the training data or train adversarial AI systems to identify errors in the AI’s model that allow it to be bypassed or exploited.
• Bias and Discrimination: AI models are built based on labeled training data. If that data contains biases — such as predominately containing images of particular demographic groups — then the AI model will learn the same biases.
• Lack of Transparency: AI can identify trends and detect complex relationships. However, its models are not transparent or interpretable, making identifying errors or biases in the final model infeasible.

How Can AI Help Prevent Cyber Attacks?

The proliferation of high-powered AI is a driving force behind tighter and more accurate security controls and workflows. Because AI can be implemented in drastically different formats according to the data it’s trained on, the following use cases are grouped according to the security tools implementing the AI.

AI In Network Security

AI’s implementation in network security can run the gamut from identifying suspicious external connections to implementing tighter network segmentation.

Automated Identity Discovery

Role-Based Access Control (RBAC) is a way of implementing network security according to the principle of least privilege.

Instead of assigning blanket permissions to static groups of individuals – which is highly time- and resource-demanding – RBAC links specific roles to the permissions that reflect their job responsibilities. Users are then assigned to these roles – automatically inheriting the associated permissions.

For instance, a new employee may be linked to the role of ’database admin’: the permissions involved would be:

• Creating and deleting databases
• Backing up and restoring data

These explicit permissions would look completely different from those in an ‘accountant’ role.

AI is accelerating RBAC adoption thanks to its ability to discover identities automatically. New network security tools may scan logins, file access, and application usage across departments, to then build a profile of what real employees are accessing day-to-day.

Should it detect that a specific group regularly accesses accounting software, handles payroll data, and runs monthly reports, it’s able to automatically suggest a “Finance Analyst” role. New employees with similar job functions can then be automatically assigned this role, streamlining RBAC onboarding.

Real-Time Threat Classification

Network security is dominated by the stateful firewall. A tried-and-tested approach that monitors the incoming and outbound connections between enterprise devices and the public Internet, they remain a bastion of security since Check Point invented them in 1993.

With AI, however, firewalls are able to automate far more of the threat detection workflow: this can be applied to both incoming traffic and when assessing the legitimacy of external sites.

For instance, AI-supported firewalls are pre-trained on labeled traffic network data.

Since the AI model becomes highly adept at recognizing and labelling malicious network activity, the firewall can link disparate policy violations into the wider picture of a real-life attack.

Next-Generation Firewalls

Next-generation firewalls take this capability beyond alert labels, and offer automated response capabilities according to the suspected attack type. This could include:

• Automated updating of internal traffic policies
• Isolating communications to an infected subnet

Last-resort response capabilities, such as moving traffic over to dedicated failover servers, must be manually added to the firewall via playbooks, to ensure business continuity.

It’s not just internal traffic that firewall AI can assess: depending on your firewall provider, some also offer URL categorization. This uses Natural Language Processing (NLP) AI to categorize URLs according to their safety.

Dangerous or inappropriate sites can be blocked at the firewall level, leading to maximum security.

Zero Day Attack Prevention

While the vast majority of attacks rely on pre-established attack vectors, there is a highly lucrative black market for zero day vulnerabilities. These are so valuable precisely because these vulnerabilities do not yet have patches.

(And when levied against firewalls, can represent a major security concern.)

An AI-enhanced firewall is able to defend against zero days by establishing a baseline of normal network activity. For instance, it’s able to plot a typical data’s worth of transfer volumes for each user role. If the firewall detects a sudden spike in data transfer to an external server at an unusual hour, it flags or blocks the activity as potentially malicious.

This same technique can also protect otherwise unpatched applications.

AI in Endpoint Security

Secure Endpoints are now an integral component to enterprise security. At its core, Endpoint Detection and Response (EDR) collects detailed telemetry from these endpoints, such as:

• Process execution
• Parent-child process relationships
• File interactions such as creation, modification, and deletion

This data is rich but complex, making it ideal for AI analysis.

Endpoint-Based Behavioral Analysis

AI enables predictive threat detection by learning what normal behavior looks like and spotting subtle anomalies that may indicate malicious activity.

This makes it particularly adept at spotting complex or tightly-engineered malware that employs obfuscation techniques like process hollowing – or even when a malicious process is named something legitimate-looking. Since EDR monitors which process is interacting with which file, its AI can then spot when a background process is accessing sensitive files it normally wouldn’t.

This deviation allows an alarm to be raised far before a successful attack is deployed.

Predictive Analytics

Because different strains of malware act in different ways, an EDR AI is able to identify trending patterns within an ongoing attack, and predict which systems or users are likely to be targeted next. For instance, if account takeover is the suspected root cause of an attack, it’s able to examine which databases the account may have access to.

If the EDR is integrated with the firewall, this can be turned automatically into corresponding firewall policy changes.

Benefits of Leveraging AI Technologies in Security

AI offers significant potential benefits for corporate cybersecurity including:

• Enhanced Threat Detection: AI can analyze large volumes of security alerts and accurately identify true threats. This enables security teams to more quickly detect and respond to potential intrusions.
• Rapid Incident Remediation: After a security incident has been identified, AI can perform automated remediation based on playbooks. This expedites and streamlines the incident response process, reducing attackers’ ability to cause damage to the organization.
• Improved Security Visibility: AI can analyze large volumes of data and extract useful insights and threat intelligence. This can provide organizations with greater visibility into the current state of their IT and security infrastructure.
• Greater Efficiency: AI can automate many repetitive and low-level IT tasks. This not only reduces the burden on IT personnel, improving efficiency but also ensures that these tasks are performed regularly and correctly.
• Continuous Learning: AI can continually learn and update its models while in active operation. This enables it to learn to detect and respond to the latest cyber threat campaigns.

AI Security Recommendations and Best Practices

Some security best practices for implementing AI include the following:

• Ensure Training Data Quality: AI is only as accurate and effective as its training data. When building AI systems and models, ensuring the correctness of labeled training data is key.
• Address Ethical Implications: AI usage has ethical implications due to the potential for bias or misuse of personal data for training. Ensure that safeguards are in place to ensure that training data is complete and the necessary consent has been granted.
• Perform Periodic Testing and Updates: AI models may contain errors or become outdated over time. Periodic testing and updates are essential to ensure AI model accuracy and usability.
• Implement AI Security Policies: Cyber threat actors may target AI systems in their attacks. Implement security policies and controls to protect AI training data and models against potential exploitation.