AI Security

The integration of Artificial Intelligence (AI) into cybersecurity practices offers transformative potential, enhancing threat detection, response capabilities, and overall security posture. However, this intersection also presents unique challenges, including ethical considerations, the potential for AI-driven attacks, and the need for robust AI security measures.
  • The global AI in cybersecurity market is projected to reach $38.2 billion by 2026, reflecting the growing integration of AI technologies in security strategies. (Source: MarketsandMarkets)
  • 82% of cybersecurity professionals believe AI and machine learning can improve threat detection and response rates. (Source: ESG Research)

Understanding AI Security: Definition and Explanation

On a basic level, artificial intelligence (AI) security solutions are programmed to identify “safe” versus “malicious” behaviors by cross-comparing the behaviors of users across an environment to those in a similar environment. This process is often referred to as “unsupervised learning” where the system creates patterns without human supervision. For some AI platforms, like Vectra, “deep learning” is another key application for identifying malicious behaviors. Inspired by the biological structure and function of neurons in the brain, deep learning relies on large, interconnected networks of artificial neurons. These neurons are organized into layers, with individual neurons connected to one another by a set of weights that adapt in response to newly arriving inputs.

Sophisticated AI cybersecurity tools have the capability to compute and analyze large sets of data allowing them to develop activity patterns that indicate potential malicious behavior. In this sense, AI emulates the threat-detection aptitude of its human counterparts. In cybersecurity, AI can also be used for automation, triaging, aggregating alerts, sorting through alerts, automating responses, and more. AI is often used to augment the first level of analyst work.

Common Applications of AI in Cybersecurity

AI security solutions have a wide range of applications in the realm of cybersecurity. Here are some of the most common uses:

  1. Threat Detection and Prediction: AI can analyze large datasets to identify activity patterns indicative of potential malicious behavior. By learning from previously detected behaviors, AI systems can autonomously predict and detect emerging threats.
  2. Behavior Contextualization and Conclusion: AI can contextualize and draw conclusions from incomplete or new information, aiding in the identification and understanding of cybersecurity events.
  3. Remediation Strategy Development: AI tools can suggest viable remediation strategies to mitigate threats or address security vulnerabilities based on their analysis of detected behaviors.
  4. Automation and Augmentation: AI can automate various cybersecurity tasks, including alert aggregation, sorting, and response. It complements the work of human analysts, enabling them to focus on more complex challenges.

Benefits of Leveraging AI Technologies in Security

The adoption of AI cybersecurity solutions offers several advantages for organizations and their IT and security teams:

  1. Enhanced Data Processing: AI's capabilities enable the processing of large volumes of data at high speed, providing organizations with comprehensive insights into potential threats.
  2. Augmentation for Resource-Constrained Teams: AI fills the resource gap for smaller or less resourced cybersecurity teams by automating routine tasks and providing continuous protection.
  3. Consistent and Long-Term Protection: AI systems provide consistent and continuous protection, reducing the risk of human error and offering long-term defense against evolving threats.

Evaluating an AI Cybersecurity Vendor for Your Network

Choosing the right AI security vendor is crucial for ensuring the effectiveness and compatibility of the solution with your network. Here are some key questions to consider when evaluating AI cybersecurity vendors:

  1. Machine Learning Algorithms: What types of machine learning algorithms does the vendor's product utilize? How frequently are these algorithms updated and new ones released?
  2. Algorithm Performance: How quickly can the machine learning algorithms detect threats in a new environment? Do they require a learning period, and if so, how long does it take?
  3. Prioritization and Integration: How does the product prioritize critical and high-risk hosts that require immediate attention? Does it seamlessly integrate with existing detection, alerting, and incident response workflows?
  4. Integration with Security Infrastructure: Does the product integrate with firewall, endpoint security, or network access control (NAC) systems to block or contain detected attacks? How well does it integrate with these platforms?
  5. Workload Reduction and Efficiency: What is the expected workload reduction for security analysts using the product? How much efficiency improvement can be anticipated?
  6. Real-World Testing: Does the product support running red team exercises to demonstrate the value of machine learning algorithms in real-world scenarios? Is the vendor willing to cover the costs if the product fails to detect any threats?
  7. Product Evaluation: Do they recommend providing remote access to human analysts during the evaluation? What are the reasons behind this recommendation?

How AI can Enhance your Cybersecurity

Combining human intelligence with data science and machine learning techniques. The Vectra AI approach to threat detection blends human expertise with a broad set of data science and advanced machine learning techniques. This model delivers a continuous cycle of threat intelligence based on cutting-edge research, global and local learning models, deep learning and neural networks.

How AI driven cybersecurity framework
How AI can streamline your cybersecurity operations

1. Capturing data

Sensors extract relevant metadata traffic or logs in from cloud, SaaS, data center and enterprise environments.

A uniquely efficient software architecture developed from Day 1, along with custom-developed processing engines, enable data capture and processing with unprecedented scale.

2. Normalizing data

Traffic flows are deduplicated and a custom flow engine extracts metadata to detect attacker behaviors. The characteristics of every flow are recorded, including the ebb and flow, timing, traffic direction, and size of packets. Each flow is then attributed to a host rather than being identified by an IP address.

3. Enriching data

Our data scientists and security researchers build and continually tune scores of self-learning behavioral models that enrich the metadata with machine learning-derived security information. These models fortify network data with key security attributes, including security patterns (e.g. beacons), normal patterns (e.g. learnings), precursors (e.g.weak signals), attacker behaviors, account scores, host scores, and correlated attack campaigns.

4. Detecting and Responding

AI scores custom-built attacker behavior models detect threats automatically and in real time, before they do damage. Detected threats are then automatically triaged, prioritized based on risk level, and correlated with compromised host devices.

Tier 1 automation condenses weeks or months of work into minutes and reduces the security analyst workload by 37X.

Machine learning-derived attributes like host identity and beaconing provide vital context that reveals the broader scale and scope of an attack. Custom-engineered investigative workbench is optimized for security-enriched metadata and enables sub-second searches at scale.

AI puts the most relevant information at your finger tips by augmenting detections with actionable context to eliminate the endless hunt and search for threats.

The Data Science Behind a Cybersecurity AI

Using behavioral detection algorithms to analyze metadata from captured packets, our cybersecurity AI detects hidden and unknown attacks in real time, whether traffic is encrypted or not. Our AI only analyzes metadata captured from packets, rather than performing deep-packet inspection, to protect user privacy without prying into sensitive payloads.

Data science behind cybersecurity AI

Global learning - using AI for cybersecurity

Global learning: Find the hidden traits that all threats share in common.

Global learning identifies the fundamental traits that threats share across all enterprise organizations.

Global learning begins with the Vectra AI Threat Labs, a full-time group of cybersecurity experts and threat researchers who continually analyze malware, attack tools, techniques, and procedures to identify new and shifting trends in the threat landscape.

Their work informs the data science models used by our Attack Signal Intelligence, including supervised machine learning.

It is used to analyze very large volumes of attack traffic and distill it down to the key characteristics that make malicious traffic unique.

Local Learning - using AI in cybersecurity

Local learning: Reveals attack patterns that are unique to the network.

Local learning identifies what's normal and abnormal in an enterprise's network to reveal attack patterns.

The key techniques used are unsupervised machine learning and anomaly detection. Vectra AI uses unsupervised machine learning models to learn about a specific customer environment, with no direct oversight by a data scientist.

Instead of concentrating on finding and reporting anomalies, Vectra AI looks for indicators of important phases of an attack or attack techniques, including signs that an attacker is exploring the network, evaluating hosts for attack, and using stolen credentials.

Integrated intelligence - using AI in cybersecurity

Integrated intelligence: Correlate, score, prioritize

Vectra AI condenses thousands of events and network traits into a single detection.

Using techniques such as event correlation and host scoring, our AI performs the following:

  • Correlates all detection events to specific hosts that show signs of threat behaviors.
  • Automatically scores every detection and host in terms of the threat severity and certainty using the Vectra AI Threat Certainty Index™.
  • Tracks each event over time and through every phrase of the cyberattack lifecycle.

Vectra AI  puts special focus on events that may jeopardize key assets inside the network or are of strategic value to an attacker. Devices that exhibit behaviors that cover multiple phases of the cyberattack lifecycle are also prioritized, as shown.

By understanding attacker behavior and patterns, Vectra reduces unnecessary alerts and focuses on the true positives. This gives security analysts the ability to effectively hunt, investigate, and stop attacks before they become breaches. In the following sections, we will explore the scope and development process of Vectra's technology, including how it collects and generates detections, correlates events into actionable incidents, and handles real attacks with two specific examples.

Developing ML Algorithm for Threat Detection

Vectra's detection system is specifically designed to find attackers and their methods in action, rather than just detecting unusual anomalies. Our team of security researchers and data scientists with diverse backgrounds have a deep understanding of extracting valuable insights from complex data sets. With over ten years of experience, we have developed a collaborative approach to threat detection that effectively identifies attacker behaviors with minimal false positives.

How Vectra develops AI Security algorithms
How Vectra develops security-led AI algorithms

Throughout the detection development process, our security research team leads the way. They constantly monitor and review the methods employed by attackers in the wild, focusing on general methods rather than specific tools or attack groups. For example, instead of solely analyzing the Cobalt Strike beacon, we abstract the actions of this technology and study the attacker's overall method of control. This allows us to build coverage for both present and future tools executing similar methods.

Once an attacker method is identified, our security researchers work alongside our data science team to gather a corpus of malicious and benign samples. Malicious samples are sourced from various places, including customers who voluntarily share anonymized metadata, publicly documented cyber incidents, synthetic data creation algorithms, and internal lab attacks. Benign samples are collected from our extensive data set of anonymized customer metadata.

With the attacker method and supporting data at hand, our security researchers and data science team develop a prototype model with an optimized threshold for detecting these methods. The prototype is deployed in a silent beta mode, gathering feedback from an opt-in customer base to fine-tune the model. Every instance of the attacker method observed, as well as events just below the threshold, are reported back, allowing our data scientists to further refine the model.

This iterative process continues until strict standards of quality are met, ensuring the model's performance in real-world scenarios. The final step involves creating a dedicated user interface that presents the full context of the identified attacker method, along with relevant information about what is normal for the systems in question. The models are then deployed into production and continuously monitored to ensure their efficacy. Any necessary improvements are made to the detection system using the same pipeline used for data collection.

The results are models that do not require frequent tuning and effectively detect current and future generations of attacker tools. Our security-led approach excels at detecting attacker actions, going beyond detecting strange events.

Real-time Streaming Engine for Actionable Results

When it comes to protecting your organization, every second counts. That's why delays in alerting can give attackers a dangerous advantage. But with Vectra's real-time streaming engine, you can stay one step ahead.

Unlike traditional batch processing, Vectra's algorithms run on streaming data, ensuring immediate detection without any delay. This means attackers have less time to progress their attacks, giving you ample opportunity to stop them in their tracks.

But it's not just about speed – it's also about scale. As the size and complexity of enterprise networks, cloud deployments, and SaaS services continue to grow, so does the amount of data that needs to be processed. This is where Vectra's real-time streaming engine shines.

Designed to support large international enterprises, Vectra's streaming engine can handle even the most massive amounts of data. It effortlessly extracts the necessary information to build long-term learning models, without any issues of data size.

And let's not forget about the power of history. Algorithms that use unsupervised learning rely on a wealth of data to be truly effective. By learning from streaming data, Vectra's algorithms are able to factor in months of historical data and millions of events. This means the highest quality alerts and the most accurate detection in the industry.

Artificial Intelligence for Threat Correlation

Vectra goes beyond identifying individual attacker methods. With our advanced AI technology, we correlate actions to swiftly detect, categorize, and prioritize actively progressing attacks. Our correlation algorithm analyzes behaviors across accounts, hosts, network, and the cloud to provide a clear signal of any security incident.

But how do we attribute these behaviors to stable anchors such as accounts or host machines? In network and hybrid-cloud environments, we utilize a groundbreaking algorithm called host-id. This algorithm allows us to attribute transient IPs to stable host machines based on observed artifacts, including Kerberos host principals, DHCP MAC addresses, and cookies. With this attribution, we can accurately identify and track attacker behavior and metadata flow associated with a specific host machine, not just the IP.

However, attribution in AWS comes with its own challenges. Events are recorded in the AWS control plane and associated with Assumed Roles, rather than underlying user accounts. This means that any number of accounts can assume a given Role, making it difficult to trace the origin of an attack. That's where our custom-built technology, Kingpin, comes in. Kingpin can unravel the chaining of Roles to attribute observed attacks to an underlying user, giving you the crucial information needed for effective response.

Once we have attributed attacker behaviors to stable indicators, we then correlate them together to identify the underlying behavioral profile of the system. This allows us to label and prioritize progressing threats for immediate attention. Our correlation algorithm mimics the actions taken by our expert analysts and security researchers, ensuring that you receive the same level of threat classification and analysis.

How Vectra AI Prioritizes Threats

Vectra AI's AI correlates threat behaviors to a host or account and prioritizes them into one of four severity rankings: Critical, High, Medium, and Low. This ranking is based on Vectra's scoring model's understanding of how aligned the collective attacker behaviors are to a real escalating attack. Security teams monitoring the Vectra console should primarily base their judgment on which hosts or accounts to review first based on the calculated severity ranking.

Severity Rankings

  • Critical or High severity: Hosts and accounts categorized as Critical or High severity have a high potential for doing damage to business operations and exhibit behaviors associated with actively unfolding attacks that warrant investigation.
  • Low or Medium severity: Accounts categorized as Low or Medium severity are exhibiting less directly observed risks and can be leveraged for starting points in threat hunting efforts rather than immediate investigation.

Threat and Certainty Scores

In addition to the severity ranking, threat and certainty scores are calculated for each prioritized account based on the correlated behaviors to enable finer-grain ordering. Detections also receive threat and certainty scores that characterize detection-specific severities based on the threat of the associated behavior and certainty of the underlying detection models. Details of how each detection's threat and certainty are calculated are presented on their respective detections one-pagers.

> Learn more on Vectra AI's Detections

FAQ

How are Malicious Actors Leveraging AI?

As enterprises adopt AI technology for cybersecurity, malicious actors are also adapting their methods to evade detection. They learn about the threat flagging systems employed by AI solutions, allowing them to modify their attack strategies and accelerate their malicious activities.

What are the Advantages of Using AI in Cybersecurity?

AI cybersecurity tools offer automated detection capabilities, enabling enterprises to efficiently identify, locate, quarantine, and remediate threats. They enhance the overall effectiveness and speed of incident response.

How is AI used in threat detection?

AI plays a vital role in threat detection by analyzing vast amounts of data and identifying patterns or anomalies that may indicate potential security threats. Through machine learning algorithms, AI systems can learn from historical data and continuously adapt their models to recognize new and emerging threats. AI-powered threat detection systems can monitor network traffic, analyze behavior patterns, and detect malicious activities in real-time, enabling organizations to proactively respond to and mitigate potential threats.

What is AI threat intelligence?

AI threat intelligence refers to the use of artificial intelligence techniques and technologies to gather, analyze, and interpret vast amounts of data from various sources, such as security logs, vulnerability databases, dark web forums, and social media platforms. By leveraging AI algorithms, threat intelligence platforms can automate the process of collecting and correlating data, identifying potential threats, and providing actionable insights to organizations. AI-driven threat intelligence enhances the speed, accuracy, and scalability of threat analysis, empowering security teams to stay ahead of evolving cyber threats.

How is AI used in cyber defense?

AI is extensively used in cyber defense to strengthen the security posture of organizations. AI algorithms can analyze large volumes of security data, including network logs, system events, user behavior, and malware samples, to identify suspicious activities or potential vulnerabilities. AI-powered systems can detect and respond to security incidents in real-time, automate threat hunting, and enhance the efficiency of security operations. Additionally, AI can be employed in developing advanced security mechanisms like behavior-based anomaly detection, adaptive access controls, and intelligent threat response systems, fortifying cyber defense against sophisticated attacks.

> Learn more about Vectra's AI security solution

Can AI prevent hackers?

While AI can significantly enhance cybersecurity measures, it cannot single-handedly prevent all hackers. AI technologies are effective in detecting and mitigating certain types of threats, but cyber attackers constantly evolve their tactics to evade detection. AI-powered systems can contribute to reducing response time, identifying vulnerabilities, and analyzing patterns, but human expertise and collaboration are crucial for effective cyber defense. Combining AI capabilities with skilled cybersecurity professionals can create a robust defense strategy that includes proactive threat hunting, threat intelligence analysis, and incident response, ultimately making it more challenging for hackers to succeed.

Vectra: Empowering Security Teams with AI

Vectra AI, a leading provider of Network Detection and Response (NDR) solutions, leverages AI technology to deliver maximum security for your systems, data, and infrastructure. By detecting and alerting your security operations center (SOC) team of suspicious activities, both on-premises and in the cloud, Vectra AI enables swift and precise action against potential threats. With AI-driven genuine threat identification, your team can focus on critical tasks, free from false alarms.

As AI continues to reshape the cybersecurity landscape, embracing these technologies while navigating their challenges is essential for enhancing organizational security. Vectra AI is at the forefront of integrating AI into cybersecurity solutions, offering advanced threat detection and response capabilities powered by AI. Contact us to explore how our AI-driven solutions can bolster your cybersecurity strategy and protect against sophisticated cyber threats.

FAQs

How is AI transforming cybersecurity?

AI transforms cybersecurity by automating complex processes for detecting and responding to threats, analyzing vast amounts of data to identify patterns and anomalies indicative of cyber attacks, and predicting future threats based on historical data. This allows for more proactive and efficient security measures.

What are the primary applications of AI in cybersecurity?

The primary applications include threat detection and analysis, anomaly detection, automated incident response, phishing detection, and security posture assessment. AI algorithms can sift through data at unprecedented speeds, identifying threats faster and with greater accuracy than traditional methods.

What challenges does AI present to cybersecurity?

Challenges include the potential for AI systems to be manipulated or bypassed by sophisticated cyber attackers, ethical concerns related to privacy and AI decision-making, and the reliance on quality data for AI training. Ensuring the integrity and security of AI systems themselves is also a significant challenge.

How can organizations protect AI-driven cybersecurity solutions from attacks?

Protecting AI-driven solutions involves implementing robust data security measures to safeguard the data used for training AI models, continuously monitoring AI systems for signs of manipulation, and employing layered security approaches to protect against potential vulnerabilities.

What ethical considerations must be taken into account when using AI in cybersecurity?

Ethical considerations include ensuring the privacy and security of personal data processed by AI systems, transparency in AI decision-making processes, and avoiding bias in AI algorithms that could lead to unfair or discriminatory outcomes.

Can AI be used to conduct cyber attacks?

Yes, AI can be used to conduct cyber attacks by automating the creation and execution of sophisticated attack strategies, enhancing the ability to exploit vulnerabilities, and evading detection by mimicking normal user behavior or adapting to defensive measures.

What is the role of machine learning in cybersecurity?

Machine learning, a subset of AI, plays a crucial role in cybersecurity by enabling systems to learn from data, identify patterns, and make decisions with minimal human intervention. This is particularly useful for detecting new or evolving cyber threats that may not be identified by traditional security measures.

How can organizations prepare for the integration of AI into their cybersecurity strategies?

Organizations can prepare by investing in AI and machine learning expertise, evaluating and updating their data governance practices, establishing ethical guidelines for AI use, and staying informed about the latest AI security threats and defense mechanisms.

What future developments are expected at the intersection of AI and cybersecurity?

Future developments may include the increased use of AI for predictive threat intelligence, the development of AI-driven autonomous security systems, greater focus on securing AI systems from attacks, and the ethical regulation of AI in cybersecurity applications.

How does AI influence the cybersecurity skills gap?

AI can help mitigate the cybersecurity skills gap by automating routine tasks and analyses, allowing cybersecurity professionals to focus on more strategic activities. However, it also creates a demand for skills in AI and machine learning within the cybersecurity field.