Risk Management & Anomaly Detection

Use case: A DeFi platform’s security team wants to implement risk and anomaly detection to protect their protocol and users. They need to catch things like unusual trading patterns, potential flash loan attacks, sudden drops or spikes in key metrics, or any behavior that deviates from the norm.

What this means: In practice, anomaly detection on-chain means setting up continuous monitoring for signals that something might be wrong. This could be purely on-chain metrics (like a gigantic minting event of tokens that shouldn’t happen, or an address suddenly accumulating a huge governance power) or patterns (like a normally inactive contract starts getting calls from many new addresses, possibly an exploit in progress).

How DeepBlock helps:

• Real-time triggers

DeepBlock’s near-real-time indexing means the security team can get data on events as they happen. They might set up triggers using the GraphQL subscription (if supported) or by polling critical queries. For example, a query could be “notify if any single address swaps more than $5 million worth of tokens on our platform within an hour” or “alert if the protocol’s TVL drops by >10% within 30 minutes.” DeepBlock can handle these kinds of aggregate computations easily, and an external script or an AI agent can continuously query and watch for threshold breaches.

• Pattern templates

With knowledge graph data, one can define patterns of an exploit. For instance, a common attack pattern might be: attacker borrows a flash loan, uses it to manipulate a price or drain a pool, then quickly moves funds through a series of addresses. DeepBlock can be pre-loaded with templates for these patterns. When an actual sequence of events matches a template (even partially), it can flag it. This is more advanced than simple threshold alerts; it’s structural anomaly detection. The graph is particularly adept at this because it understands sequences and relationships (e.g., an address interacting with a governance contract and then with a lending contract in short succession can be a red flag).

• Risk scoring

DeepBlock can also assist in assigning risk scores to addresses or transactions. For example, by cross-referencing with known lists (scam addresses, sanctioned addresses, etc.) in the graph, any interaction with such entities could raise a risk score. Or if an address suddenly interacts with a mixer after interacting with your protocol, that might increase its risk level (perhaps indicating they are trying to cover tracks after exploiting something). You can maintain an attribute on address nodes like riskLevel and update it via queries as certain conditions are met. Over time, DeepBlock could become a repository of reputational data too (which could be used to, say, pause or review transactions involving high-risk entities).

• AI-driven analysis

Once an anomaly is detected, an AI agent using DeepBlock can help triage it. For instance, if an alert triggers that “TVL dropped 15% in 10 minutes”, an AI could automatically query: “which pool or asset had the largest outflow? which addresses were involved?” and present a quick explanation: “TVL drop was primarily due to a single address withdrawing 10k ETH from the main pool, likely an outlier event. That address has history with the protocol (a known large liquidity provider). No other unusual activity detected.” This type of contextual explanation is invaluable to avoid false alarms or to know immediately if something warrants deeper investigation.

Outcome: By leveraging DeepBlock, the DeFi platform’s security team moves from reactive to proactive. They have a system that continuously watches the chain for them and can not only flag issues but also provide insight on them instantly. This shortens response times dramatically, potentially preventing or minimizing damage from attacks. It also provides peace of mind to users (which could even be communicated, e.g., “monitored by DeepBlock for anomalies” as a security badge). Overall, it’s like having a 24/7 security analyst who never tires, powered by live data and AI smarts.