Statistical sampling compares cached values against source-of-truth in real-time. Detects poisoning, corruption, and drift. Auto-repairs individual keys. Reports a consistency score per key prefix.
Your cache serves millions of responses per second. Not one of them is verified. The cached value could be wrong, stale, or maliciously injected — and nobody would know until a customer reports it.
A background process continuously samples cached keys, fetches the authoritative value from the source-of-truth, and compares them. Mismatches are auto-repaired. Everything is reported as a consistency score per key prefix.
Self-healing consistency runs as a background sampling loop. It does not intercept reads. It does not add latency to writes. It does not sit in the hot path. Your GET and SET operations are completely unaffected. The sampling rate is configurable per key prefix — sample 100 keys per minute for low-traffic prefixes, 10,000 per minute for critical ones.
Repair is surgical. When a mismatch is detected, only the diverged key is re-fetched and updated. There is no full invalidation, no cache flush, no thundering herd. One key is wrong, one key is fixed.
Sampling 1,000 keys per minute from a namespace of 1 million keys gives you statistically significant coverage. Within an hour, you have sampled 60,000 keys. Within a day, 1.44 million — more than your entire keyspace. Any systematic corruption or drift surfaces quickly through the consistency score.
Random sampling catches both targeted attacks (a specific key is poisoned) and systemic drift (a CDC pipeline is dropping events). The probability of a poisoned key surviving undetected decreases exponentially with each sampling cycle.
Cache poisoning is an attack vector that nobody talks about. An attacker who compromises a cache write path can serve malicious data to every user. Self-healing is the only defense that exists.
No other caching system detects cache poisoning. Redis does not verify its own data. Memcached does not sample keys. Hazelcast does not compare cached values to source. Every cache in production today trusts its own data unconditionally. Self-healing consistency is the first mechanism that treats the cache as an untrusted data store and continuously verifies it against the authoritative source.
For enterprise security teams evaluating caching vendors, this is a differentiated capability. Being the cache vendor that detects and auto-repairs cache poisoning is a pitch that no competitor can match.
Every key prefix gets its own consistency score. Green means healthy. Yellow means investigate. Red means alert. One number that tells you whether your cache is serving correct data.
Four categories of teams where cache correctness is not optional.
Self-healing consistency is a background process that statistically samples cached keys and compares their values against the source-of-truth (database, API, or upstream cache). When a mismatch is detected, the cache automatically re-fetches the correct value and updates the entry without requiring a full cache invalidation. The process reports a consistency score per key prefix so operators can monitor cache accuracy in real time.
Cachee's self-healing consistency engine continuously samples cached values and compares them to the authoritative source. If an attacker compromises a cache write path and injects malicious data, the statistical sampling detects the mismatch within seconds. The poisoned key is automatically repaired, the divergence is logged with full context (timestamp, key, expected value, actual value), and an alert is sent to the security team.
A consistency score is the ratio of sampled cache keys that match their source-of-truth value, expressed as a percentage. Cachee calculates this per key prefix and exposes it on the dashboard. A score of 99.99% means your cache is healthy. A score of 99.9% warrants investigation. A score below 99% triggers an alert. The consistency score provides continuous, auditable proof that your cache is serving correct data.
No. Self-healing consistency runs as a background process on a configurable sampling schedule. It does not intercept or add latency to any cache read or write operation. The sampling, comparison, and repair all happen asynchronously. Your GET and SET operations are completely unaffected.
TTL expiration blindly evicts keys after a fixed time period, regardless of whether the cached value is still correct. Self-healing consistency verifies whether the value is actually correct by comparing it to the source-of-truth. Correct values stay cached. Incorrect values are repaired in place. This means you get higher hit rates (no unnecessary evictions) and stronger correctness guarantees (actual verification instead of time-based guessing).
Self-healing consistency detects poisoning, catches drift, auto-repairs corruption, and gives you a consistency score you can put in front of auditors.