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Jan 16, 2024

AI in Cybersecurity: Revolutionizing MXDR - Insights from Gradient Cyber

Understanding MXDR in the Age of AI

Managed Extended Detection and Response (MXDR) is a critical defense mechanism against increasingly sophisticated cyber threats. By definition, it ingests large amounts of disparate security and IT management data, subjects that data to an ever-evolving set of analytics, and then presents findings to human analysts,  who - as final arbiters of the decision-making platform - convert analyses into specific response and remediation action.


It would seem every part of that process can and should be fully automated, especially with the advent of artificial intelligence (AI). After all, even the parts best left to humans come down to fuzzy logic - partial truths where the truth value may range between completely true and completely false. That too can presumably be handled by neural networking where forward and backward propagation learns to handle nuanced decisions rapidly. So does AI magically end the $10.5 trillion cybercrime market?


Why AI Alone Can't End Cybercrime

In a word, no. Here are three reasons why:


  1. Evolving Nature of Cyber Threats: Cybercriminals are constantly developing new strategies and tools to evade detection. They adapt quickly to the latest security measures, often exploiting zero-day vulnerabilities (flaws in software that are unknown to the vendor) and using sophisticated techniques like polymorphic malware, which changes its code to avoid detection by AI-based systems. AI relies on existing data and patterns to make predictions and decisions; thus, it may struggle to promptly identify and respond to novel or rapidly evolving threats.

  2. AI Requires Quality Data: AI and machine learning models are only as good as the data they are trained on. If the training data is biased, outdated, or lacks representation of certain types of cyber threats, AI will have blind spots. This limitation means AI might fail to detect certain attacks or generate false positives, leading to inefficient use of resources or overlooked threats. Furthermore, cybercriminals can use techniques like data poisoning to deliberately skew data from which AI systems learn, thereby reducing their effectiveness.

  3. Human Element and Social Engineering: A significant portion of cybercrime involves social engineering tactics, which manipulate human psychology rather than exploiting technical vulnerabilities. AI has limitations in understanding and predicting human behavior, especially in complex social contexts. Phishing attacks, pretexting, baiting, and other forms of social engineering can often only be detected through human intuition and experience, areas where AI does not excel. Moreover, the final decision-making in complex and nuanced situations often requires human judgment, which AI cannot replicate. Ok, artificial general intelligence (AGI) - a hypothetical type of intelligence - could learn to accomplish any intellectual task that human beings or animals can perform. And, perhaps given the extraordinary advancements from GPT-3 to GPT-3.5 to GPT-4, we can assume this is no longer a ‘hypothetical’. But amongst the very best minds in the world, the jury remains out on ‘when’.

The Role of Human Intelligence in AI-Driven Cybersecurity

That argues the industry must continue leveraging AI as the ‘second brain’ to even the most accomplished cyber analysts - which for the foreseeable future will (and must) remain the Human-in-the-loop (HITL). Of course, but how? Let’s get specific.


Four Pillars of AI-Enhanced MXDR at Gradient Cyber

At Gradient Cyber, we think of AI as a means of advancing four tenets of MXDR:

  • Reduced mean time to detect (MTTD)
  • Reduced mean time to response (MTTR)
  • Improved human understanding of AI analyses - increasingly aided by natural language processing (NLP)
  • Greater security stack integration and orchestration - both of which reduce the cost of security through security orchestration automation and response (SOAR)

Gradient Cyber's Strategic AI Roadmap for Enhanced MXDR

Our XDR platform roadmap is shaped by a vision of cutting-edge MXDR purpose-built for mid-market needs and budget tolerance. That means every quarter or so we assess what will deliver the greatest impact for our customers across platform infrastructure, security stack integrations, reporting, UI/UX, threat intelligence sources and analytics. Virtually every part of our platform has been, or will be, influenced by AI in our mission to stay on the forefront of advanced threat detection and response. While the world became hyper-aware of AI with the arrival of ChatGPT in November 2022, the cybersecurity industry has been leveraging AI for years. Gradient Cyber is no exception. Our development team has been steadily enriching our XDR platform with AI, well before ChatGPT became a household term. Notably, our developers have been making consistent improvements to our MXDR solution by incorporating:

  • Supervised and unsupervised machine learning (ML) for detecting patterns and anomalies
  • Predictive analytics that use data, statistical algorithms, and ML techniques to identify the likelihood of future outcomes based on historical data
  • AI-driven anomaly detection to identify unusual patterns or behaviors in a network which could indicate a security threat

2024 Outlook: Expanding AI Applications in MXDR

Here in 2024, we intend to expand our use of AI on four fronts:

  • Workflow Automation: Repetitive tasks such as threat intelligence documentation and situation report (Sitreps as we call them) generation will be automated, thereby streamlining our operations and significantly reducing response times. The integration and automation of disparate security tools will further free our cybersecurity analysts (CAs) to focus upon strategic threat mitigation and complex problem-solving.
  • Threat Detection and Analysis: In-house, privacy-controlled Large Language Models (LLMs) will be used to comb extensive data sets for anomalies and patterns indicative of cyber threats, enhancing our interpretive capabilities and enriching our analyses, allowing us to categorize and prioritize threats with greater precision.
  • Predictive Analytics: Advanced AI and ML technologies will enable us to customize alerting methodologies. These methodologies will be informed by historical data and patterns, ensuring our customers receive the most relevant and timely alerts tailored to their unique security postures and preferences.
  • Orchestration: We will use LLMs to chain and process data in an integrated manner from different platforms and monitoring solutions, ensuring a unified defense against cyber threats.

Conclusion: AI's Transformative Impact on MXDR

By embracing AI and its potential, coupled with our HITL expert insights, we provide detection and response solutions that are not only advanced, but also adaptable to the evolving threat landscape. We invite mid-market IT security professionals to collaborate with us in fortifying their digital infrastructure, leveraging our state-of-the-art MXDR solutions.

Find out more about our AI-infused MXDR solution.

Neal Hartsell

Neal Hartsell is Chief Marketing Officer at Gradient Cyber. His career spans three decades where he has held senior leadership roles in product management, product marketing, and digital marketing in networking and cybersecurity companies. He has served in senior roles across industry giants such as Cisco, 3Com, and Northern Telecom. Neal held CMO, VP Marketing, and product management and marketing roles at startup, early-stage, and growth companies including ECI Telecom, Crossroads, TippingPoint, RGB Networks, NSS Labs, Netgate, NetSpeed (acquired by Cisco), Surgient Networks, RipCode (acquired by RGB Networks), and Click Security (acquired by Alert Logic). Neal has also served as a strategic marketing consultant to a number of cybersecurity startups.