Many discussions about artificial intelligence (AI) today trend to swing toward one extreme or the other: AI is either a revolutionary force or a harbinger of job displacement. It will either be really good for us or really bad, Terminator-style.
However, the reality is much more nuanced. There is no single, inevitable AI future. Rather, the impact of AI will depend less on its capabilities and more on how we choose to wield it.
This is especially true in the cybersecurity space. AI has great potential to make the threat landscape more manageable for security teams, so they can more effectively prevent and mitigate major cyber events. However, cybersecurity teams will need to take a thoughtful and strategic approach to integrating AI into cyber operations to maximize its impact.
At ThreatConnect, we believe that to effectively fight cyber threats moving forward, security teams will need to rethink their processes, workflows, and their own roles to find the right balance where AI enhances but doesn’t replace human expertise.
Understanding AI’s Strengths and Weaknesses in Cybersecurity
In general, AI’s strengths lie in processing vast data volumes, pattern recognition, and automation. For cybersecurity teams, this might translate to machine learning models that can detect anomalies and patterns that humans might miss, AI-driven automation that reduces workloads for analysts, and more accurate, faster threat prioritization through AI-driven classification and scoring of threat indicators.
On the other hand, anyone who has experienced a hallucination knows that AI is not infallible. It can lack the necessary context to manage nuanced threats and it can miss novel threats due to training on historical data. AI models and data can also drift over time or be compromised by adversaries.
What this means is that AI is not a “silver bullet” tool that cybersecurity teams can simply set and forget. Without regular human intervention and oversight, AI is likely to misclassify threats and generally prove ineffective in the cybersecurity space.
Instead, cybersecurity teams should think of AI as a collaborator and find the best ways to use human expertise, intuition, and creativity to compensate for AI’s weaknesses while leveraging its strengths.
Perfecting the Human-AI Collaboration Equation
Teams need to carefully rethink their existing work to identify the best way to integrate and make the most of AI. For example, at ThreatConnect, we design AI solutions and tools that work seamlessly into teams’ existing threat intel lifecycles. In this way, teams aren’t left to reinvent processes and procedures every time AI systems evolve. Instead, AI enhances existing, proven workflows in new ways.
When thinking about integrating AI at your own company, it can be helpful to think about AI as the world’s fastest intern—incredibly helpful, but needing regular supervision and training.
For example, here are a few best practices to foster greater AI-human collaboration in your cybersecurity operations:
- Establish human feedback loops: AI models should regularly incorporate analyst input to improve over time.
- Practice continuous monitoring: AI insights require regular validation to maintain accuracy.
- Deploy rigorous testing: AI-driven threat intelligence must be vetted to avoid blind spots.
- Commit to frequent model updates: Environments, inputs, and expectations are always changing. Consider frequent model updates to ensure AI adapts to evolving threats.
- Don’t forget end-user input: Sometimes people use tools differently than intended. Listen to end-user input to shape AI to meet real-world needs.
- Build AI talent and expertise: As AI proliferates, security teams must understand how AI systems work and where they pose risks.
How ThreatConnect Balances Man and Machine with Practical AI
At ThreatConnect, we believe that cybersecurity teams need AI solutions that enhance—not replace—their expertise so they can make faster, smarter, and more informed decisions. As a result, we take a dynamic, use case-driven approach to building AI solutions for security teams.
In practice, that looks like building solutions around three core AI capabilities and mapping those solutions into the threat intelligence lifecycle. Those AI capabilities include:
- Correlation: Uncover meaningful relationships across vast datasets and CTI frameworks to improve prioritization, context, and decision-making.
- Classification: Automatically tag, categorize, and contextualize threat intelligence to align with frameworks like MITRE ATT&CK.
- Acceleration: Reduce technical barriers to action through customizable automation and by distilling large volumes of intelligence, enabling teams to act faster
As a result, our AI technology is deeply integrated into the ThreatConnect ecosystem, supporting intelligence operations with automated decision support, classification, correlation, scoring, and summarization. To date, more than 250 enterprises worldwide trust our AI solutions to help them stay ahead of adversaries.
Choosing a Future of AI Collaboration
In many cybersecurity spheres, machine learning and AI are not necessarily new technologies. However, they’re increasingly becoming must-have resources in today’s threat landscape.
By thinking critically about the best ways to wield AI—to shore up its weaknesses with human intuition, context, and creativity while leveraging its strengths—cybersecurity teams can maximize the impact of AI now and in the future.
About the Author
Dan Cole is the VP of Product Marketing of ThreatConnect. He spent two decades as a product manager, developing a deep understanding of user and market needs. This expertise helps him evangelize the value of threat intelligence and ThreatConnect to cybersecurity teams across the globe, ensuring that our software resonates deeply with our users and that they can get the most out of our products. Outside of work, Dan is a Star Wars enthusiast, a wildlife (fox!) photographer, and an indulgent foodie. Dan can be reached online at [email protected] and at our company website https://www.threatconnect.com/
