Challenges Posed By AI in Cybersecurity
AI’s ability to incorporate and generate a great deal of data quickly can make it a significant hazard when deliberately used for malicious ends.
These following AI-driven threats can make the task of securing an enterprise more difficult.
Phishing LLMs
Social engineering manipulates individuals or organizations into revealing sensitive data or compromising security by exploiting trust and human error. Techniques like phishing are central to this, where attackers impersonate legitimate sources to deceive victims into:
- Clicking harmful links
- Sharing personal details
- Transferring funds
By creating a false sense of trust or urgency, attackers can access networks, devices, and accounts without needing to breach technical defenses, relying instead on the ease of manipulating human behavior.
Advanced AI models like OpenAI’s GPT-4 are capable of generating highly personalized spear phishing emails at scale, with incredible realism and minimal cost. This combines the efficiency of mass phishing with the precision of targeted attacks and, as a result, enables cybercriminals to deploy custom attacks that appear authentic. The hit rate of these phishing messages therefore becomes far higher, funding more cybercrime.
The rise of multimodal generative models, which can interpret and create content across:
This provides attackers with even more attack avenues than just email. These models enable attackers to generate highly personalized phishing content by analyzing targets’ images and audio samples.
For example, Microsoft’s VALL-E can clone a person’s voice from a brief audio sample, allowing attackers to fabricate convincing voice phishing (vishing) calls and mimic associates. This convergence of multiple modalities – text, image, and voice – greatly expands the scope and realism for potential attacks.
CAPTCHA
CAPTCHA checks have typically been a way of keeping bad bots out, and ensuring only humans are interacting with your online resources.
But, AI’s ability to continuously learn has seen researchers create a bot that’s able to bypass reCAPTCHA v2.
Combining an object-recognition model with a VPN (to disguise repeated attempts from one IP address), a natural mouse movement tool, and fake cookie data, their AI-powered tool has been able to simulate human-like browsing behaviors. As a result, this tool beat 100% of all CAPTCHA tests.
Inhouse AI Development Increasing Security Risk
It’s not just AI in cyberattackers’ hands to be concerned about: the security risks of in-house AI tools can be significant. For example, training data leaks present a significant vulnerability, as seen when Microsoft researchers unintentionally exposed around 38 terabytes of sensitive data on GitHub.
This incident occurred because permissions on an Azure Storage URL for downloading AI models mistakenly granted access to the entire storage account. The exposed data included sensitive information, such as:
- Personal backups
- Service passwords
- Secret keys
- 30,000+ internal Microsoft Teams messages
This illustrates the potential severity of accidental data exposure in AI research – and why you have to be careful.
AI’s Benefits to Cybersecurity
In the right hands, AI-driven tools are able to negate not just new AI threats, but also some of the more traditional difficulties that have plagued security teams for years.
Ability to Analyze Lots of Data
Security Information and Event Management (SIEM) solutions centralize log collection and analysis from across an organization’s defenses. aggregating data, SIEMs help:
- Identify anomalies
- Trigger alerts
- Gather additional context
- Isolate affected assets
This enables organizations to respond swiftly and effectively to potential security incidents.
Machine learning and pattern recognition give AI-enabled SIEM systems the power to analyze historical security data far faster than manual reviews can: even better, all of this individual data can then be used to build a behavioral baseline for each user and device.
Unlike traditional SIEMs, which depend on log signatures and predefined criticalities, AI SIEMs dynamically detect threats by comparing current events to this baseline. Plus, these systems can then analyze abnormal patterns to establish any connection to known attack vectors, enabling highly-accurate, near-real time alerts.
Phishing Threat Detection
The primary objective of a phishing attack is to mimic a legitimate website closely, as this similarity helps deceive victims into believing they are on a trusted site.
This imitation leads victims to unknowingly provide sensitive information directly to attackers, such as:
- Passwords
- Financial details
The more convincing the phishing page appears, the higher the chances of successfully manipulating the victim into taking harmful actions.
Multimodal large language models (LLMs) that process both text and images have great potential for detecting brand-based phishing. With extensive training on various brand representations, these models analyze a webpage’s visual and textual elements to recognize its brand association.
This capability enables LLMs to identify inconsistencies in brand presentation that might indicate a phishing attempt, potentially improving automated detection methods for brand-based cyber threats.
Data Loss Prevention
The importance of securing how those AI tools apply their training data has never been more vital. Because AI prompts and their responses take the format of conversational, unstructured data, they’re incredibly difficult to keep secure with keywords – as traditional DLP methods use.
Advanced AI-based data classification can help safeguard sensitive information by detecting and preventing potential data leaks associated with these applications, ensuring that full control over platforms’ data flows is maintained.
How Check Point Grants Full-Security AI Integration
To understand GenAI applications in an organization, it’s crucial to identify and assess the risks these tools may pose, especially around data security and compliance. Carefully weighing up AI’s pros and cons within your own use cases is key to determining its long-term suitability.
Check Point’s GenAI security is able to keep all of your teams’ AI projects secure by accurately classifying and securing conversational data within prompts. If you’ve already been building a number of tools, it provides in-depth visibility into your GenAI application and all connected services. This cuts the risk of shadow IT, and helps further stay ahead of regulatory demands. Join our Preview Program today to explore your AI toolings’ sprawl.
As for Check Point’s application of AI within pre-existing cybersecurity tools, our Infinity platform harnesses generative AI to deliver advanced threat prevention, streamlined automated threat response, and effective security management. By combining 50 AI engines to cover the full breadth of threat intelligence, Check Point Infinity delivers the best catch rate of any security solution. When any new Indicator of Compromise is discovered, the pattern is quickly applied to the entire tech stack in under 2 seconds, securing:
- Clouds
- IoT, networks
- All other endpoints from even zero-day attack
Fundamentally, Check Point Infinity supports AI innovation: securing both new-found applications, and supporting the complex services that AI-powered organizations run on. Schedule a demo to see how it works.
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