Unlocking the Potential: Large Language Models in Security Operations Centres
Part 2: Building vs. Consuming – Decoding the Options
As organizations contemplate integrating LLMs into their security frameworks, a critical decision arises – to build and train their own models or to consume LLM capabilities via security platform providers. Part two of our series weighs the pros and cons of both options, offering a comprehensive guide for end-users to make informed choices that align with their unique cybersecurity needs.
Welcome back to our exploration of Large Language Models (LLMs) in Security Operations Centres (SoC). In this second instalment, we confront a pivotal decision that organisations must make: should they embark on the journey of building and training their own models, or opt for a more streamlined approach by consuming LLM capabilities via security platform providers.
Part 2: Building vs. Consuming – Decoding the Options
Building Your Own
Pros:
1. Tailored to Specific Needs: Building an in-house LLM allows customization to match the unique requirements of your organization. Specific to your market vertical, your customers and how your organisation delivers value to your customers
2. Complete Control: Organisations retain full control over the training data, model architecture, and fine-tuning processes. Assurance that the model is secure, and your organisations data remains within your security domain.
3. Deep Integration: In-house models can be seamlessly integrated into existing workflows, ensuring a cohesive cybersecurity environment. With support for your own bespoke applications the in house model can drive value from the investment in tailored systems.
Cons:
1. Resource-Intensive: Building and maintaining a robust LLM requires a significant investment in talent, infrastructure, and ongoing maintenance. Unless you have a DevOpps orientated approach your organisation will need to invest in skilled resources.
2. Expertise Demands: Organisations need access to experts in machine learning, natural language processing, and cybersecurity to ensure effective development. The Industry right now is hiring these roles and unless you have filled these in your own organisation you can add 3-6months hiring the right team.
3. Time-Consuming: The process of training and optimizing an LLM from scratch can be time-consuming, potentially delaying implementation. The accuracy of the model is based upon your own data sources, ensuring you “train” the model with the correct assets and artefacts is critical to its ability to provide accurate and relevant output.
4. Isolated: LLM’s that do not benefit from training on the largest set of data may be less adept at making determinations vs LLM’s that are open & cross-industry. With limited data sources as your reference point the model will contextualise responses based solely upon your industry or vertical or organisation. Whilst this might appear valuable to remain initially focused on specific data points, any data model is far less reliable without a far greater context.
Part 2: Building vs. Consuming – Decoding the Options
Consuming via Security Platform Providers
Pros:
1. Plug-and-Play Integration: Leveraging LLM capabilities from established platform providers offers a quick and seamless integration process. With pre-defined integrations and commercial models to cater for consumption at scale.
2. Continuous Updates: Platform providers often update their models to adapt to emerging threats, ensuring that your cybersecurity stays current. Keeping relevant with sprint updates to the models to give a greater relevance to reality. Whilst they will never keep pace with zero day threats, the techniques, and tactics will follow a pattern of behaviour that an up-to-date model can help determine.
3. Reduced Resource Strain: Organizations can sidestep the need for in-house expertise and the associated resource burden. Platform providers have already built the teams to design and support the LLMs and are developing new models at pace and scale, allowing your organisation to consume on demand without having to build new teams or departments.
Cons:
1. Less Customization: While effective, pre-built LLMs might offer less customization compared to in-house models. The ability to tailor to your specific industry or organisations needs will need to be evaluated as to how much value that brings.
2. Dependency on Providers: Organizations become dependent on the roadmap and updates of the chosen security platform provider. Fully understanding the impact of updates and a provider’s ability to deliver the service has to be a consideration for any SaaS investment, the balance between availability and viability of running the service in house.
3. Subscription Costs: Using external services may involve ongoing subscription costs that organizations need to factor into their cybersecurity budget. Whilst the free LLMs go some way to offer an insight into their ability to deliver value. The true cost is often overlooked when engaging commercially, this has an impact on how many clicks, how many words and how verbose the output could be. If not fully considered there is a potential to destroy the business case for investment. Consideration must be given to the use cases specific to your needs.
Part 2: Building vs. Consuming – Decoding the Options
Stay tuned as we navigate the future of cybersecurity!
As we navigate this decision-making process, organizations must weigh these factors against their specific needs and resources. In the next instalment, we’ll delve into the risks associated with integrating LLMs in SoCs and strategies to mitigate them effectively.
If you are reading this now, you are so are someone who is fascinated in all things AI and LLM’s. Exciting news, we have a perfect event for you!
Cybanetix is kicking off their event season with an AI community roundtable, where all guests can openly share their thoughts and queriers in front of industry experts.
Head over to the sign-up page below to book your seat at the roundtable.