Secure tomorrow:
The role of AI in cybersecurity

While AI is accelerating cybersecurity risks, find out how you can harness its power to stay ahead.

Cyberattacks are on the rise.

300%

increase in detections of advanced phishing attacks by Barracuda in 2023.

icon

48%

of organizations experienced phishing or social engineering attacks in 2023.*

icon

47%

of advanced phishing attacks are missed by Microsoft native security.

AI enables cybercriminals to scale attacks and avoid detection.

The cyber kill chain framework is used to understand and describe various stages of cyberattacks, such as ransomware, from initial reconnaissance to data exfiltration. Hackers use AI to scale up their attacks through automation, improve targeting, and hide lateral movement across the network.

Preparation

AI will automate the collection and analysis of data to identify potential targets and vulnerabilities, craft emails, and generate malware designed to evade detection.

Phishing and Account Takeover

AI is used to craft more convincing and personalized phishing emails, increasing the likelihood of successful attacks.

Network Compromise

AI will automate the process of identifying and exploiting vulnerabilities in target systems while adapting in real time to the target’s responses, making attacks difficult to stop.

Lateral Movement

AI algorithms guide attackers to prioritize certain targets for lateral movement and help to determine what data is valuable before it’s encrypted.

Data Exfiltration

AI can optimize extracting and exfiltrating sensitive data, making it more challenging for security systems to detect unusual behavior patterns and circumventing traditional defenses.

Ransom Demand

After the data is encrypted and the ransom is demanded, AI can automate the interaction between the victim and the attacker.

Previous

Preparation

AI will automate the collection and analysis of data to identify potential targets and vulnerabilities, craft emails, and generate malware designed to evade detection.

Phishing and Account Takeover

AI is used to craft more convincing and personalized phishing emails, increasing the likelihood of successful attacks.

Network Compromise

AI will automate the process of identifying and exploiting vulnerabilities in target systems while adapting in real time to the target’s responses, making attacks difficult to stop.

Lateral Movement

AI algorithms guide attackers to prioritize certain targets for lateral movement and help to determine what data is valuable before it’s encrypted.

Data Exfiltration

AI can optimize extracting and exfiltrating sensitive data, making it more challenging for security systems to detect unusual behavior patterns and circumventing traditional defenses.

Ransom Demand

After the data is encrypted and the ransom is demanded, AI can automate the interaction between the victim and the attacker.

Next

Outsmart AI with AI

image
AI techniques make phishing and social engineering attacks easier to detect, and logs with credential access can be analyzed faster for anomalies. Sifting through network-level traffic data can be done more efficiently with natural language processing. Detection of lateral movement, suspicious files, folders or system activities is easier. Security teams can use AI to detect threats earlier in the cyber kill chain, making their defenses and responses more effective than traditional security measures.
icon

Improved threat detection and intelligence

Machine learning algorithms analyze email traffic and network activity to establish a company’s baseline behavior and then identify anomalies related to potential attacks, such as unusual traffic, emails, or unexpected user behavior. AI’s pattern recognition capabilities excel in identifying complex attack patterns, recognizing evolving techniques, and using predictive analysis to anticipate future threats.

image

Superior detection efficacy of phishing attacks

AI-powered email security leverages data from established phishing patterns to identify malicious emails that traditional gateways miss. It analyzes anomalies in email behavior, such as irregular sender behavior, while natural language processing evaluates message content for sentiment, context, tone, and potential malicious intent. The result is enhanced detection accuracy of personalized phishing attacks, including those generated using AI techniques.

 image

Effective in-the-moment security awareness training

While traditional training regimens generally adhere to a periodic schedule, involving simulations or fabricated attacks, GenAI can help provide targeted, personalized, in-the-moment training to end users. Users will be offered tailored resources and chat support to provide context on the attack they are being targeted with. This method appeals to users and saves time for security teams, providing more effective and intuitive training.

image

Faster incident response through automation

Deploying AI in security operations offers faster response to threats and incidents. AI-driven systems operate more efficiently in real time, correlateing signals across attack surfaces to disable attacks sooner while reducing human error. Current applications include automating incident identification, orchestrating playbook automation, and increasing the effectiveness of SOC teams by enhancing threat detection and response.

Stronger application security

AI and machine learning improve bot detection by reducing the number of false positives typical of more traditional solutions. AI-powered application security will more accurately detect initial access and reconnaissance attempts by identifying potential zero-day attacks and alerting IT admins while automatically blocking the attacks.

The future of security lies in the fusion of human expertise and AI. The ability to analyze vast real-time datasets and identify patterns makes AI an indispensable asset in the defender’s toolkit.

Vikas Arora

SVP Global IT and Security, Toluna

Get AI-powered security from Barracuda.

At Barracuda, we use AI-powered security to help organizations fight the most sophisticated threats. Our AI uses advanced content analysis techniques, anomaly detection, and natural language processing to recognize malicious activity and identify anomalies in user behavior. The continuous learning aspect of AI ensures adaptability to the evolving threat landscape to refine and improve detection efficacy over time. The dynamic, self-learning nature of the AI minimizes false positives and enhances overall email security.

image

AI-powered email security

AI-powered protection relies on content analysis, anomaly detection, and natural language processing. These techniques scrutinize emails in real time for malicious intent, such as content sentiment, recognizing known phishing patterns and identifying anomalies in sender behavior.

image

AI-powered Web Application and API Protection

Machine learning (ML)-powered detections are used to identify and block advanced attacks, including account takeover on applications. In addition, ML-powered API discovery capabilities identify unprotected shadow and zombie API endpoints and automatically turn on protections. ML-powered auto configuration engine uses live traffic to identify and fine-tune application protection settings to reduce admin fatigue and improve security.

image

AI-powered XDR & SOC

With cutting-edge ML to establish baselines in diverse environments, Barracuda’s XDR solution and SOC (Security Operations Center) teams achieve high precision anomaly detection. These ML algorithms analyze patterns within the organization’s data, helping identify deviations that could signal serious security threats.
image

Securing tomorrow:
A CISO’s guide to the role of AI in cybersecurity

See the impacts of ariticifical intelligence on both cyberattacks and defenses.