I heard a quote last week that summed up our industry’s predicament perfectly: “We’re trying to solve 21st-century problems with 20th-century thinking.”
It’s right. We’ve spent the last two decades layering more tools, more rules, and more complexity onto fundamentally broken approaches. Meanwhile, other industries have undergone complete transformations by rebuilding from first principles with modern technologies.
It’s time we did the same.
What If We Started Over?
Imagine if we approached cybersecurity the way Netflix approached recommendations or Visa approached fraud detection — not by patching existing systems, but by reimagining what’s possible from the ground up.
When Netflix wanted to transform entertainment, they didn’t just build a better video rental store. They rebuilt the entire experience and today they are built around foundation deep learning models. Netflix found that different model architectures excel at different tasks, and only when they built vertical spacific models did they get the results they were needing. Their recommendation engine is fundamentally a foundation model that learns patterns across millions of users and applies that knowledge to provide personalized recommendations.
When Visa wanted to fight fraud, they didn’t just add more rules to existing systems. They built foundation deep learning models that adapt to new patterns of fraud as they emerge. These aren’t simple rule-based systems — they’re sophisticated foundation models that process vast amounts of transaction data to understand normal payment behavior and detect anomalies in real-time. Today, Visa processes over 150 million transactions daily with fraud rates below 0.1% precisely because their foundation deep learning models can identify fraudulent patterns that traditional systems miss.
Both companies succeeded by building foundation deep learning models that learn, adapt, and improve automatically — exactly what cybersecurity needs but has never achieved at scale.
The Foundation Model Revolution
The technology that can finally break us out of our current trap already exists: foundation models.
Unlike the narrow AI tools we’ve been bolting onto broken systems, foundation deep learning models represent a fundamentally different approach. Training specialized models from the ground up for narrow tasks, engineers can develop new applications for their specific use case. This is the same approach that made Netflix’s recommendation engine and Visa’s fraud detection so powerful — foundation models that understand fundamental patterns and can be applied across diverse scenarios.
Think about it: every organization is essentially fighting the same war against the same adversaries, yet we’re all learning in isolation with our individual tools and data lakes. Foundation deep learning models enable collective defense at unprecedented scale — the same way Netflix’s foundation models learn from all users to benefit individual recommendations, or Visa’s foundation models learn from all transactions to protect individual payments.
The First Glimpse of What’s Possible
Companies like DeepTempo are showing us what this future looks like. Their LogLM (Log Language Model) isn’t just another security tool — it’s a Foundation Model uniquely able to rapidly adapt to new environments while retaining extraordinary accuracy.
Here’s what makes this approach totally unique:
Collective Intelligence: Foundation models, like Tempo LogLM, can apply what they learn in one environment to each new environment they encounter. They are collective intelligence — at work in defense of us all.
Continuous Learning: Foundation models adapt very quickly to novel environments. In practice, this means much shorter tuning times than traditional deep learning systems which are notorious for taking weeks or even months to tune for a given environment and then lose their accuracy when the environment changes.
Pattern Recognition at Scale: LogLMs ingest huge volumes of data and detect very long and subtle patterns or sequences that deviate from learned normal behavior.
The results speak for themselves. In one case study involving a large financial institution, projected savings reached several million dollars, representing a major impact on their existing SIEM spending.
Rethinking Security Architecture from First Principles
Here’s the paradigm shift: instead of building security systems that try to identify every possible threat, we build systems that understand normal behavior so deeply that any deviation becomes immediately apparent.
This is exactly how your immune system works. It doesn’t maintain a database of every possible pathogen — it recognizes what belongs and what doesn’t. When something foreign appears, your immune system responds immediately, even to threats it’s never encountered before.
Traditional Approach:
- Maintain massive rule databases
- Signature-based detection
- Reactive response to known threats
- Isolated learning per organization
- High false positive rates
Foundation Deep Learning Model Approach:
- Learn patterns of normal behavior
- Anomaly-based detection
- Proactive response to novel threats
- Collective intelligence across organizations
- Sub-1% false positive rates
The Netflix Model for Cybersecurity
Netflix’s transformation offers a perfect blueprint. The same process that Netflix uses to generate tailored film recommendations can be applied to cybersecurity: gather massive amounts of historical data on both legitimate and threatening behaviors, then build foundation deep learning models to find which data points (or combinations of data points) are most important, and how they should be weighed.
Netflix gathered massive amounts of diverse data: viewing history, search behavior, device information, time patterns, content metadata, and much more. Then they built foundation deep learning models that could understand and predict user preferences with unprecedented accuracy. These aren’t simple recommendation algorithms — they’re sophisticated foundation models that learn from billions of user interactions across the entire platform.
We can do the same for cybersecurity.
Breaking Free from Tool Sprawl
Remember our earlier finding that security teams today employ 31.58 tools on average, each requiring time and resources for security experts to manage? Foundation models offer a path to radical simplification.
Instead of dozens of specialized tools, imagine a single foundation deep learning model that:
- Ingests data from across your entire environment
- Understands the relationships between different systems
- Learns your organization’s unique operational patterns
- Provides unified, contextual threat detection
- Adapts continuously to new threats and changes
DeepTempo takes open approaches, running on any data lake, and reducing lock-in from aging vertically integrated security platforms. This is the key: foundation deep learning models can unify disparate data sources and provide holistic security intelligence without forcing organizations into vendor-specific ecosystems.
The Collective Defense Advantage
Here’s where foundation deep learning models become truly transformative: they enable collective defense at global scale.
A foundation model is a form of collective defense: because it’s trained on insights from many environments (while preserving privacy), it gains a sort of community immune system effect.
Think about the implications: when an attacker develops a new technique and uses it against any organization participating in the collective defense network, the foundation deep learning model learns from that attack and immediately protects all other organizations — without sharing sensitive data or compromising privacy.
This is fundamentally different from traditional threat intelligence sharing, which relies on after-the-fact indicators and manual processes. Foundation deep learning models create real-time, automated collective learning that scales globally.
The Economics of Transformation
The financial case for this transformation is compelling. Consider the 31.58 different tools statistic from earlier and 69% of large organizations seeing an increase in tools from 2022 to 2023 according to the 2023 Gartner Technology Adoption Roadmap for Large Enterprises Survey.
Foundation deep learning models offer a different economic model:
Traditional Approach Costs:
- 30+ separate security tools and licenses
- Integration and maintenance overhead
- Specialized staff for each tool category
- High false positive rates driving manual investigation costs
- Reactive breach response and recovery
Foundation Deep Learning Model Economics:
- Single foundational platform with multiple applications
- Automated learning and adaptation reducing manual tuning
- Unified skill requirements and training
- Sub-1% false positive rates minimizing investigation overhead
- Proactive threat prevention reducing breach costs
DeepTempo’s cost reduction compared to traditional SIEM spending provides a concrete example of what’s possible when we stop patching broken systems and start building intelligent ones.
Overcoming the Implementation Challenge
The biggest barrier isn’t technical — it’s psychological. After decades of incremental improvements and vendor promises, security leaders are understandably skeptical of “revolutionary” solutions.
But consider this: every other major technology transformation has followed the same pattern. Early adopters who embraced cloud computing, mobile platforms, or e-commerce gained massive competitive advantages while late adopters struggled to catch up.
The cybersecurity transformation is following the same trajectory. In 2025, a fundamental component of this strategy is to build an effective identity fabric, a product-agnostic integrated set of identity tools and services. When done right, this will be a welcome relief to security professionals, taming the chaos and risk caused by a proliferation of multicloud environments and scattered identity solutions.
Organizations that start building foundation deep learning model-based security architectures now will have significant advantages:
- Lower operational costs through automation and efficiency
- Better threat detection through collective intelligence
- Reduced human burnout through more accurate systems
- Faster adaptation to new threats and business requirements
- Competitive advantage through superior security posture
The Path Forward
The transformation won’t happen overnight, but it needs to start now. As stated in the TV show the The Six Million Dollar Man. “We have the technology.” So lets take advantage of it and use it to create a better future as well as a better today.
The Choice Before Us
We stand at a crossroads. We can continue patching our broken systems, burning out our people, and losing the war against increasingly sophisticated adversaries. Or we can embrace the same technological revolution that has transformed every other aspect of business and society.
The technology exists. The early results are promising. The economic case is clear. The only question is whether we have the courage to abandon approaches that have never worked in favor of solutions built for the world we actually inhabit.
Netflix didn’t become the dominant entertainment platform by building a better Blockbuster. Visa didn’t revolutionize payments by creating better paper checks. And we won’t solve cybersecurity by adding more layers to fundamentally broken systems.
It’s time to build security from first principles with the tools of the 21st century. It’s time to stop playing defense with 20th-century approaches and start building systems that learn, adapt, and protect at the speed of modern threats.
The transformation starts with recognizing that the way forward isn’t through incremental improvement — it’s through fundamental reimagining of what cybersecurity can be when we build it right from the beginning.
The future of cybersecurity isn’t about better tools. It’s about better intelligence. And that intelligence is finally within our reach.
