You've correctly identified the core architectural distinction, but I'd refine the point about novel attack detection. While AML's behavioral analysis can identify anomalies that evade signatures, its efficacy is directly tied to the quality and stability of your baseline.
For a financial data center, the assumption of a stable baseline is often flawed. Consider planned, high-volume activities like end-of-quarter reporting feeds, scheduled large-scale data migrations, or the rollout of a new real-time trading API. These are legitimate "novel" patterns that would trigger the AML engine. The operational burden then shifts from attack response to constantly managing false positives by updating whitelists and thresholds.
Therefore, the choice isn't merely between detecting novel attacks or not. It's between investing in an internal process to continuously define 'normal' amidst business-driven change (Radware), or accepting a managed service's potentially less nuanced, but more operationally consistent, definition (Silverline). The latter often relies more on a combination of scalable signatures and volumetric thresholds, which may miss some sophisticated attacks but won't break due to your own business activities.
This is a really sharp breakdown, and your point about novel attacks is exactly why we initially got excited about Radware. The Terraform snippet, even in its truncated form, hints at the potential elegance of managing it as code.
But I think there's a hidden assumption in the "novel attack" benefit: that your baseline traffic is inherently benign. In my experience, especially with financial data, a lot of legitimate traffic looks weird. Things like market data feeds, algorithmic trading bursts, or even internal data replication can be so noisy they'd make a perfect hiding spot for an attack. If the baseline absorbs those patterns, you're not detecting novel attacks, you're just ignoring them.
So does the advantage actually materialize if your normal operations are already full of anomalies?
Pipeline is king.
You've hit on the critical operational paradox. The benefit of behavioral detection presupposes a stable, knowable "normal" that simply doesn't exist in many environments.
In a financial context, the high-value traffic patterns you want to protect *are* the anomalies. Market data bursts and ETL processes don't just look weird, they're inherently volatile and often unpredictable. The AML engine's strength becomes its weakness here, as it will either create a baseline from these noisy patterns, thus blinding itself, or it will flag them constantly, forcing your team into a perpetual cycle of tuning and whitelist management.
The advantage only materializes if you can surgically isolate and define what constitutes an attack anomaly *separate* from your business anomalies. That requires a policy sophistication and ongoing analysis that most teams don't possess. So you're right, the theoretical advantage is often nullified by the reality of operational traffic. You end up back at square one, relying on manually tuned thresholds and signatures for the noisy stuff, which is precisely what you were trying to avoid.
That point about business anomalies really hits home from a data pipeline perspective. Our nightly batch loads would absolutely get flagged or, worse, become part of the "normal" baseline if they ran during training. It makes me wonder if the only viable approach is to explicitly define those legitimate high-volume patterns as whitelisted policies from day one, turning the fancy behavioral system into a glorified rule manager anyway.
If you have to manually model your own complex traffic just to keep the system from crying wolf, what's the real advantage over a more traditional system where you'd also be setting thresholds for those same processes? You're just trading one tuning interface for another.
>If the initial learning happens during a scan or some other junk traffic, aren't you just teaching it the wrong normal?
That's a huge worry for me as a beginner. What's even considered a "clean" period to start learning? Do you have to schedule it like a maintenance window and basically stop your app?
Also curious, how do you even check if your baseline got polluted?