5 ways AI is being used to improve security: Application security

We’ve arrived at the fifth post in our series on cybersecurity and AI. This time, we’re talking about application security and how AI improves these defenses against attack.

What is application security?

Application security encompasses the protection of applications throughout the entire application lifecycle. This includes everything from design and development to deployment and ongoing maintenance. Application security is critical to cybersecurity because so many applications are accessible via the public internet or a company network. Here are some examples:

• Web applications - social media platforms, e-commerce sites, and online banking portals.
• Mobile applications - messaging, navigation and mapping, fitness tracking, and mobile banking applications.
• Enterprise applications - customer relationship management systems and enterprise resource planning software.
• Cloud-based applications - file storage and sharing, customer service platforms, and collaborative applications like Microsoft 365.

It’s difficult to determine the number of applications in use today. The total number of applications referenced above, plus IoT and other custom or internal applications, gives you a rough estimate of nearly 10 million unique applications. That’s just the number of applications that are available. Each of these applications has anything from one to nearly 700 million downloads as of 2023. This is a massive attack surface.

While application security is not ‘one-size-fits-all,’ organizations should always employ these fundamental components.

• Authentication: Verifying the identity of users accessing the application.
• Authorization: Controlling what actions authenticated users can perform.
• Encryption: Protecting sensitive data during transmission and storage.
• Input validation: Ensuring the safety and validity of the data entered into the application.
• Error handling: Preventing information leakage through error messages.
• Logging and monitoring: Tracking application activity to detect suspicious behavior. See Tushar’s blog on the Twilio breach for more on the importance of this.

Beyond these fundamentals, industry professionals and nonprofit organizations continue advancing standardized security approaches. OWASP, for example, has made significant contributions to education, standardization, and community collaboration.

OWASP is the Open Web Application Security Project, a nonprofit foundation that has played an important role in raising awareness of secure software and application security. OWASP publishes several documents and guides through its many projects. The widely recognized Top Ten and API Security Top 10 will help you secure your application and learn more about the domain of application security. There is also an OWASP Mobile Application Security site with a security testing guide and other resources.

The application attack surface

The application attack surface refers to the totality of different points where an attacker can attempt to access a system or transmit data to and from an environment. To understand this better, imagine a neighborhood with several houses, each with multiple doors and windows. The total number of doors and windows would be the attack surface. This is a simple but effective way to illustrate this concept.

The Software Development Lifecycle (SDLC) is a process developers use to design, develop, and test high-quality software. The SDLC aims to produce a high-quality software system that meets or exceeds customer expectations, reaches completion within times and cost estimates, and works efficiently and effectively in the current and planned Information Technology infrastructure. A fully AI-enhanced software lifecycle may include the following stages:

Implementing security at every stage of the software development lifecycle ensures potential vulnerabilities are identified and mitigated early on.

The role of AI in application security

Artificial intelligence performs many roles in application security. The following capabilities are considered the primary benefits of AI integration:

• Advanced Threat Detection and Prevention: AI can analyze vast amounts of data in real time to identify anomalies and patterns that may indicate security threats. Unlike traditional rule-based systems, AI can detect previously unknown and sophisticated attacks by learning from historical data and adapting to new threats.
• Real-time Monitoring and Response: AI continuously monitors application activities, enabling real-time detection and response to potential threats. This minimizes the window of opportunity for attackers and reduces the impact of cyberattacks.
• Enhanced Incident Response: AI automates and streamlines incident response processes, allowing for rapid identification, analysis, and containment of security breaches. This reduces downtime and mitigates potential losses.
• Reduction in False Positives: Traditional security systems often generate numerous false positive alerts, overwhelming security teams. AI can accurately distinguish between genuine threats and benign activities, significantly reducing false positives and allowing security personnel to focus on real threats.
• Predictive Analysis: AI’s data analysis capabilities extend beyond the present moment. By analyzing historical and real-time data, AI systems can predict future cyber threats, enabling organizations to take preemptive measures.

We can dig into each of these further to see how it all works.

Advanced Threat Detection and Prevention

• Establishing baselines: Machine learning models analyze normal application behavior, user actions, and data flows to create a baseline of typical activity.
• Real-time monitoring: The AI system continuously monitors application traffic, user behavior, and system events.
• Pattern recognition: Advanced algorithms identify deviations from established baselines that may indicate potential security threats.
• Contextual analysis: AI considers various contextual factors to reduce false positives and accurately identify genuine anomalies.

Real-time Monitoring and Response

• Immediate mitigation: AI can automatically block suspicious IP addresses, terminate malicious sessions, or restrict user access in real time.
• Adaptive security policies: Machine learning models can dynamically adjust security rules based on evolving threat patterns.
• Intelligent alerting: AI prioritizes and categorizes security alerts, helping security teams focus on the most critical threats.
• Predictive defense: AI can anticipate and preemptively defend against potential future attacks by analyzing historical data and attack patterns.

Enhanced Incident Response

• Real-Time Threat Detection: AI can detect abnormal behavior and identify emerging risks by leveraging machine learning and pattern recognition.
• Automated Incident Triage and Prioritization: AI and machine learning assess, categorize, and rank security incidents based on their severity, potential impact, and urgency.
• Autonomous Response and Remediation: Machine learning and AI detect, analyze, and respond to threats without human intervention.
• Continuous Learning and Adaptation: AI and ML algorithms can analyze historical security and threat intelligence data to identify attack patterns and implement preventative measures.

Reduction in False Positives

• Machine Learning for Pattern Recognition: ML uses algorithms and statistical models to identify patterns and regularities in data. This allows the solution to recognize complex patterns and establish a baseline of normal behavior.
• Contextual Analysis: ML can improve accuracy and reduce false positives by considering factors like the time of day and user roles alongside other benchmarks.
• Natural Language Processing (NLP): NLP techniques enable AI systems to comprehend comments and documentation within the code, further enhancing their ability to distinguish between real vulnerabilities and harmless code elements.
• Continuous Improvement Through Feedback: Developers and analysts provide feedback, allowing AI models to improve their ability to differentiate between true and false positives.

Predictive Analysis

• Analyzing Historical Data: AI systems analyze historical data from past security incidents, user behavior, and network traffic to identify patterns and trends that may indicate potential threats.
• Real-time Data Correlation: By continuously monitoring real-time data, AI can correlate current activities with known threat patterns and predict potential security breaches.
• Threat Intelligence Integration: AI integrates threat intelligence feeds to stay updated on the latest threat vectors and attack methods. This helps predict and mitigate emerging threats.
• Continuous Learning and Adaptation: AI systems continuously learn from new data and receive input from analysts on false positives and new threats. Models are updated regularly to improve predictive accuracy.

The AI under the hood

Machine learning is the foundation of AI in application security, but other types of AI significantly enhance it. Here are the most common types of AI used to augment machine learning:

• Deep learning: Improved accuracy in pattern recognition and better analysis of visual data for detecting manipulated images or other security purposes. Deep learning is a subset of machine learning.
• Natural Language Processing: Improves security by analyzing log files, security alerts, and text files. It also analyzes user queries and commands to identify potential injection attacks.
• Generative AI: Automates the generation of secure code snippets, generates potential attack scenarios, and can assist in creating policies and documentation.
• Reinforcement learning: Enables adaptive defense strategies and optimized security configurations and conducts automated testing to identify vulnerabilities. 

Combining AI technologies with traditional machine learning approaches provides more comprehensive threat detection and faster response times. This multifaceted approach is necessary to maximize application security and reduce the application attack surface.

Barracuda can help

Barracuda Application Protection is a complete and fierce defense for web applications and APIs. AI-enhanced security protects your applications and enables secure application delivery whether your deployment is on-premises, in the cloud, or a hybrid environment. Visit Barracuda Application Protection for more information.

Did you know?

Barracuda has a new e-book titled Securing tomorrow: A CISO’s guide to the role of AI in cybersecurity. This e-book explores security risks and exposes the vulnerabilities that cybercriminals exploit with AI to scale up their attacks and improve their success rates. Get your free copy of the e-book here.