Memory Drift Is the Silent
AI Failure.
Context windows degrade. Embeddings drift. Cached inferences become stale. RAG retrieval indexes change. Your AI system's "memory" is constantly shifting without anyone noticing. Cachee provides continuous integrity verification so you know when memory changes and whether the change is legitimate.
What Memory Integrity Means for AI
AI systems do not have a single "memory." They have context windows, embedding stores, inference caches, RAG indexes, fine-tuning checkpoints, and operational state. Each of these changes over time. Without integrity verification, these changes are invisible.
Embedding Drift
Embedding models are updated. The same text now maps to a different vector. Semantic similarity scores shift. RAG retrieval results change. Downstream outputs change. Nobody is alerted because the embedding store does not track its own integrity.
Stale Cached Inferences
An inference was computed six hours ago. The underlying data has changed three times since then. The cached inference is still being served as if it were current. The system appears to work. The results are wrong. No staleness detection exists.
RAG Index Corruption
A document is updated in the source system. The RAG index is not re-indexed. The retrieval returns the old chunk. The model generates an answer based on outdated information. The user receives a confidently wrong response with no indication of the data freshness problem.
Same Input. Different Output. Which Is Correct?
Model A at time T1 produced output X. Model A at time T2 produces output Y for the same input. Without memory integrity, you cannot tell which is correct, what changed, or whether the change was intentional.
Without Cachee: Responses B and C are served without any indication that the output has changed. Users and operators see different answers on different days with no explanation.
With Cachee: Drift is detected at T3 (index change) and T5 (embedding change). Alerts fire. The operator can investigate whether the change is correct before it reaches users.
Continuous Hash Verification of AI State
Cachee continuously verifies the integrity of cached computations. When outputs diverge from historical baselines, when cached results age past configurable thresholds, when the underlying data changes — you are alerted before the drift reaches production.
Hash Verification of Cached Computations
Every cached computation is bound to its inputs via a SHA3-256 hash. When the same computation is requested, Cachee verifies that the inputs have not changed since the result was cached. If the inputs have changed, the cached result is flagged as potentially stale. This is not a TTL — it is a content-aware integrity check that detects actual changes, not elapsed time.
Output Divergence from Historical Baselines
Cachee tracks output hashes over time for recurring computations. When the same input produces a different output, the divergence is measured and classified. Expected divergence (model was updated intentionally) can be whitelisted. Unexpected divergence (embedding drift, index corruption, data poisoning) triggers an alert. You see drift the moment it happens, not weeks later in a user complaint.
Configurable Age Thresholds
Set per-key or per-namespace staleness thresholds. When a cached result ages past the configured threshold without being refreshed or re-validated, Cachee fires a staleness alert. Thresholds are configurable per computation type: embeddings might have a 24-hour threshold, inference results a 1-hour threshold, and RAG chunks a 15-minute threshold. No single TTL fits all AI memory.
Cryptographic Integrity Proof
Every integrity verification produces a signed attestation via H33.ai. The attestation proves that at a specific time, a specific computation was verified against its inputs and the result was intact (or had drifted by a measured amount). This attestation is independently verifiable and can be presented to auditors, regulators, or downstream consumers.
Prevent Old Results from Replacing Current Ones
A replay attack substitutes an old cached result for the current computation. If an attacker can serve a stale cached inference instead of the current one, they can control AI behavior without touching the model. Cachee prevents this.
Cache Substitution Attack
An adversary replaces a cached inference result with an older version. The model appears to function normally, but it is serving outdated or manipulated results. Without replay protection, the system cannot distinguish a legitimate cached result from a replayed one. The attack is invisible to monitoring that only checks "did the cache return a result?"
Nonce Binding
Every cached result includes a cryptographic nonce that is unique to the computation request. When a result is served from cache, the nonce is verified against the current request context. A replayed result from a previous request will have the wrong nonce. The mismatch is detected before the result is served. The cache entry is flagged and the computation is re-executed from source.
Stale Result Injection
An attacker injects a cached result that was valid at time T1 but is no longer valid at time T2. The result passes content verification because it was a legitimate computation — just not a current one. This is particularly dangerous for financial models, compliance checks, and safety-critical AI where "correct last week" is not "correct today."
Temporal Validity Windows
Every cached result has a cryptographically committed validity window. The window is set at computation time and signed into the cache entry. A result cannot be served outside its validity window. Attempting to serve an expired result fails verification. The window is not a TTL that can be overridden — it is a signed commitment that is part of the entry's cryptographic binding.
Where AI Memory Integrity Matters
RAG Integrity Verification
Verify that RAG retrieval results are based on current source documents. When a source document changes, Cachee detects that the cached chunk hash no longer matches. The stale chunk is invalidated and retrieval returns fresh content. No more confidently wrong answers from outdated retrieval.
Embedding Drift Detection
Track embedding stability over time. When an embedding model is updated, Cachee measures the drift across all cached embeddings. You see the impact of a model update before it affects production retrieval. Drift above a configurable threshold triggers alerts and optional rollback to the previous embedding version.
Model Output Consistency Monitoring
Monitor whether the same inputs produce consistent outputs over time. Track output hash distributions across model versions, deployment configurations, and time windows. Detect when a model's behavior has shifted — whether from a weight update, a data pipeline change, or infrastructure drift — before users report the inconsistency.
Cache Poisoning Prevention
Detect and prevent cache poisoning attacks where an adversary inserts malicious results into the cache. Every cache entry is hash-chained and signed. An entry that was not produced by a legitimate computation will not have a valid signature. An entry that was modified after caching will not have a valid hash chain link. Poisoned entries are rejected before they are served.
Related Pages
Tamper-Proof AI Memory — AI memory that cannot be silently changed.
Verifiable Agent Execution — Trace and verify every action an AI agent takes.
AI Infrastructure Hub — All Cachee solutions for AI systems.
Detect Drift Before It Reaches Production
Continuous integrity verification. Replay protection. Staleness alerts. Deploy in minutes.