NikoTak – Tamara Shostak's blog

When I wrote the second part of my False Positives series in 2021, I focused on optimizing outcomes through traditional ML approaches. Today, as I look at how transformer architecture is reshaping our field, I see even more exciting possibilities for connecting security decisions with business outcomes. Let me share my evolving perspective on this crucial challenge.

The Evolution of Threat Detection

Our journey through threat detection approaches has been fascinating:

Traditional Approaches:

• Static rulesets (ACLs, blocklists)
• Statistical anomaly detection
• Basic ML analysis

Current State (2021):

• ML-based behavioral analytics (UEBA)
• Automated parameter tuning
• Business metric integration

Where I See Us Going:

• Transformer-based contextual understanding
• Multi-modal threat analysis
• Real-time adaptive defense with business awareness

What excites me most is how transformer architecture is helping us transcend the traditional tradeoffs between accuracy and usability. When I first wrote about this, we had to choose between powerful-but-complex statistical approaches and simpler-but-limited static rulesets. Now, I see a future where we can have both power and accessibility.

Beyond Simple Accuracy Metrics

I’ve always believed that focusing solely on the False Positive Rate (FP / (FP+TN)) or even overall accuracy ((TP+TN) / (FP+TP+FN+TN)) misses the bigger picture. What fascinates me now is how transformer models can help us understand the context behind each decision, moving us beyond binary classifications to nuanced, business-aware security decisions.

The real breakthrough I’m working on combines transformer architecture with what I consider one of my most important insights: using business outcomes as security metrics. Let me explain how this evolution is unfolding:

Traditional ML Approach (2021):

• Monitor conversion rates against security decisions
• Use UEBA for feedback loops
• Adjust algorithms based on business impact

Transformer-Enhanced Future:

• Understand the full context of user journeys
• Predict business impact of security decisions
• Create adaptive policies that optimize for both security and business outcomes
• Learn from global patterns while maintaining local business relevance

Business-Centric Security Intelligence

What truly excites me is how we’re moving beyond simple correlations between blocked requests and conversion rates. The transformer architecture allows us to:

1. Understand User Intent:

• Process entire user sessions as sequences
• Identify legitimate patterns of behavior
• Distinguish between genuine customers and sophisticated bots

1. Predict Business Impact:

• Evaluate the potential cost of false positives in real-time
• Understand the business context of each security decision
• Adapt policies based on business priorities

1. Create Adaptive Defenses:

• Learn from global traffic patterns
• Maintain customer-specific optimizations
• Balance security needs with business goals

The Future of False Positive Reduction

As we continue this journey, I see several key developments on the horizon:

1. Contextual Security Decisions:
   Instead of binary allow/block decisions, our systems will understand the full context of each request, including:

• Historical user behavior
• Business value of the transaction
• Current threat landscape
• Organizational risk tolerance

1. Automated Optimization:

• Real-time policy adjustment based on business metrics
• Continuous learning from global patterns
• Automatic adaptation to changing business conditions

1. Unified Security Intelligence:

• Integration of security and business metrics
• Holistic understanding of user journeys
• Predictive analysis of security impacts

Practical Implementation

What makes me particularly optimistic is how these theoretical advances are becoming practical realities. We’re building systems that can:

• Process complex user behaviors in real-time
• Understand the business context of security decisions
• Adapt automatically to changing conditions
• Maintain high security while optimizing for business outcomes

The key insight I’ve gained through this journey is that security solutions must evolve beyond simple threat detection to become business enablers. With transformer architecture, we’re finally getting the tools to make this vision a reality.

Looking Ahead

As we continue to develop these capabilities, I’m excited about the possibilities for creating security systems that are both more intelligent and more business-aware. The future isn’t just about better accuracy metrics – it’s about security that truly understands and supports business objectives.

I believe we’re moving toward a world where false positives won’t just be rare – they’ll be predictable and manageable within the context of business goals. That’s the future I’m working to build, and I invite you to join me on this journey.