Generative AI’s potential to create new content such as audio, code, and text can democratize security, empowering users of varying skill levels to generate solutions for their unique use cases. CrowdStrike’s Charlotte AI is a new application of this concept in cybersecurity.
“CrowdStrike has pioneered the use of artificial intelligence in cybersecurity to identify adversary behavior and combat sophisticated attacks to stop breaches,” said CrowdStrike president Michael Sentonas. “With the introduction of Charlotte AI, we’re delivering the next innovation that will help users of all skill levels improve their ability to stop breaches while reducing security operations complexity.”
What Charlotte AI Brings to the Table
Charlotte AI offers users the ability to pose natural language questions and receive answers from CrowdStrike’s Falcon platform. It employs a continuous human feedback loop across CrowdStrike’s various solutions, including Falcon Complete Managed Detection and Response (MDR), Falcon OverWatch, CrowdStrike Services, and Falcon Intelligence.
The AI harnesses security data from trillions of security events captured in the CrowdStrike Threat Graph and asset telemetry across users, devices, identities, and cloud workloads. According to CrowdStrike, Charlotte AI enables IT and security professionals to make swift, informed decisions, bridging the cybersecurity skills gap and accelerating their incident response.
But what does this mean for managed service providers (MSPs) and managed security service providers (MSSPs) who are considering leveraging generative AI for cybersecurity?
Enhanced Cybersecurity Services
Generative AI presents the opportunity for MSSPs to significantly upgrade their cybersecurity services. It can model patterns in network traffic, identifying deviations that could indicate a cyberthreat. By training generative AI models on vast datasets, MSPs can equip them to identify even the most subtle signs of malicious activity, enabling faster response times and minimizing damage.
Charlotte AI’s features, for instance, enable users to ask questions in natural language and receive rapid responses. This boosts the efficiency of incident response and threat identification, so MSPs can offer their clients a more secure and reliable service.
Bridging the Cybersecurity Skills Gap
According to the Bitdefender 2023 Cybersecurity Assessment, the increasing sophistication and regularity of ransomware and phishing attacks are constantly raising the pressure on cybersecurity teams. These teams struggle to consistently deliver expertise that stays ahead of rapidly evolving threat actors. A report by Fortinet on the cybersecurity skills gap in 2023 highlights that up to 68% of organizations deal with additional risks due to cybersecurity skill gaps and shortages.
To solve this challenge, generative AI has the potential to democratize access to cybersecurity expertise. Tools like Charlotte AI can help users of all skill levels improve their ability to halt breaches, essentially making every user a power user. This capability can significantly help MSPs and MSSPs in addressing prevalent cybersecurity skills gaps, empowering them to deliver advanced security services without necessarily requiring a team of high-level experts.
“If executed properly, this could go a long way in helping companies overcome the shortfall in qualified cybersecurity workers,” said Sterling Auty, senior research analyst at SVB MoffettNathanson, about Charlotte AI.
Staying Ahead of the Curve
End users increasingly rely on their MSPs to not only manage their IT infrastructure but also offer innovative solutions to enhance their security, efficiency, and competitiveness. Given this trend, MSP adoption of emerging technologies like generative AI is becoming an expectation rather than an exception. Remember, it’s not just the technological curve MSPs need to stay ahead of; they also need to be ahead of cyberthreats.
By adopting generative AI, MSPs can offer their clients the most impressive security protection available, a selling point that could set them apart from competitors who fail to adopt this technology. On this topic, Sterling Auty added: “We have been falling behind on being able to identify and deal with security threats, but AI done correctly could level that playing field and the company(ies) that do it right and first will likely gain the lion’s share of the benefits.”
Reduced Complexity in Security Operations
Generative AI technologies can streamline the process of managing complex cybersecurity operations. By automating routine tasks and rapidly generating unique security content, these technologies can take some of the burden off MSPs’ in-house teams. This allows them to focus more on strategic initiatives and customer relations, potentially leading to better service quality and increased client satisfaction.
Risk Management, Scalability, & Growth
These AI models can also provide MSPs with better tools for managing cybersecurity risks. By leveraging large amounts of security data and learning from it, AI models can identify patterns and predict potential threats, enabling proactive risk management.
Additionally, generative AI can enhance the scalability of MSPs. As these AI tools learn and adapt over time, they can handle increasing volumes of security events and more complex scenarios. This ability allows MSPs to effectively grow their business and client base without compromising the quality of their cybersecurity services.
Generative AI Comes with Downsides
For all of its surging popularity, generative AI is not without its shortcomings and aspects worthy of scrutiny.
Firstly, generative AI can be highly complex for MSPs that want to leverage these AI models. This complexity can pose a challenge for MSPs, especially if they lack the in-house expertise to implement and manage the technology effectively. MSPs need to invest in training or hire specialists who can handle the intricacies of these AI models.
There are also data privacy and security concerns around generative AI, as it relies on vast amounts of data to learn and evolve. To prevent these privacy and security issues, MSPs need to ensure they have robust measures in place to safeguard the data used by generative AI systems. They also need to comply with data protection regulations, which can vary significantly across different jurisdictions and industries.
Additionally, AI systems have been notorious for reflecting biases based on the data they are trained on, leading to skewed or unfair outcomes. These systems can also be unpredictable. As an MSP, you need to be aware of this potential issue and implement strategies to minimize AI bias. You also need to consider ethical aspects related to AI use, such as explainability, transparency, and accountability. Particularly keep in mind the potential reputational nightmare that may result from AI that operates contrary to your organization’s values.
Integrating generative AI into existing IT systems can be a complex process. MSPs have to ensure AI systems can work effectively with their current technology infrastructure. They should take time to carefully plan, test, and make potentially significant adjustments to existing systems before getting started.
While generative AI offers many benefits, it’s not a silver bullet that can vanquish all cybersecurity issues instantly. Currently, it is important for MSPs to manage expectations, both within their organizations and with their clients, about what the technology can realistically achieve. They should also be aware that implementing generative AI can be a costly endeavor, particularly in the early stages. Costs can include the purchase of AI solutions, hiring or training staff, and making necessary system upgrades.
With the growing awareness and potential of generative AI, we can expect to see a trend of MSSPs adopting generative AI and adding the technology into their cybersecurity offerings. However, MSPs need to develop extensive approaches to guide the implementation and adoption of generative AI, not just in cybersecurity but across all industries and business use cases. Cognizant is among the MSPs approaching this implementation thoughtfully.
“It is fast becoming clear that businesses must embrace AI without delay to remain competitive,” said Prasad Sankaran, EVP of software and platform engineering at Cognizant. “This is an exciting moment, as Cognizant’s Neuro AI platform goes beyond proof of concept, aiming to accelerate the adoption of enterprise-scale AI applications, increase ROI potential, minimize risks, and get to better business solutions faster.”
Cognizant recently launched the Cognizant Neuro AI Platform to enable companies to responsibly deploy generative AI on an enterprise scale.