AI Encryption Optimization Agent

Strong encryption without the latency tax. Dynamically tunes cipher mode, key size, and chunk size to your workload.

Overview

What it does

Continuously evaluates your encryption workload — file size, network conditions, CPU pressure, throughput SLA — and recommends or auto-applies the optimal cipher configuration in real time.

Particularly effective for streaming media, real-time financial messaging, and large medical imaging files where naive AES-256-GCM defaults add unacceptable latency. Switches modes intelligently (e.g., ChaCha20-Poly1305 on mobile, AES-256-GCM where AES-NI is available, chunked streaming for large blobs).

Returns a recommended config + an estimated latency/throughput delta. Optional: deploy the config directly via the AKRUM control plane.

How it works

A 4-step recommendation flow

Submit profile

POST workload profile (payload size, target throughput, device class, network type) to /v1/agents/encryption-optimization/recommend.

Simulate configs

Agent simulates ~12 candidate cipher configurations against your profile.

Score candidates

Scores each on latency, throughput, security level, and CPU cost.

Return best config

Returns the best config + the next two runner-up configs + projected metrics.

Sandbox

Try it live

Adjust workload parameters and run the simulated recommendation engine.

Interactive demo — simulated responses.See API pricing

Inputs

Workload typeAverage payload sizeDevice classTarget throughput (Mbps)Security tier required

Response

{
  "recommended": {
    "cipher": "AES-256-GCM",
    "chunk_size_bytes": 16384,
    "key_size_bits": 256,
    "estimated_latency_ms_p50": 4.2,
    "estimated_throughput_mbps": 850,
    "rationale": "Balanced default: AES-256-GCM with 16 KB chunks on AES-NI-enabled servers."
  },
  "alternatives": [
    {
      "cipher": "AES-128-GCM",
      "chunk_size_bytes": 16384,
      "key_size_bits": 128,
      "estimated_latency_ms_p50": 3.1,
      "estimated_throughput_mbps": 980,
      "rationale": "30% lower latency when 128-bit security is sufficient."
    },
    {
      "cipher": "ChaCha20-Poly1305",
      "chunk_size_bytes": 32768,
      "key_size_bits": 256,
      "estimated_latency_ms_p50": 4.8,
      "estimated_throughput_mbps": 760,
      "rationale": "Non-AES-NI fallback with competitive throughput."
    }
  ],
  "evaluated_at": "2026-01-15T10:00:00.000Z"
}

Simulated response — no real inference is performed.

Data sources

Benchmarked against open performance data

OpenSSL speed benchmarks (publicly published per-platform numbers)
libsodium performance documentation
Linux kernel crypto API benchmarks
NIST SP 800-38D (GCM mode performance characteristics)
Cloud provider compute benchmarks (AWS Graviton, Azure, GCP public spec sheets)
Mobile hardware encryption acceleration specs (Apple Crypto, ARM Cryptography Extension)
Streaming protocol specifications (HLS, DASH, WebRTC, RTMP)

All sources are public. AKRUM re-benchmarks against new chip releases and library updates.

API reference

Schema

Field	Type	Required	Description
workload_type	enum(streaming_video\|real_time_trading\|medical_imaging\|iot_telemetry\|backup_batch)	yes	Category of the workload being optimized.
payload_size	enum(<1KB\|1-100KB\|100KB-10MB\|10MB-1GB\|>1GB)	yes	Typical or maximum payload size range.
device_class	enum(server_with_aesni\|server_no_aesni\|mobile_arm\|embedded)	yes	Hardware profile of the target device.
target_throughput_mbps	integer	yes	Desired throughput in megabits per second.
security_tier	enum(standard\|high\|government)	yes	Required security classification.
network_type	string	no	Optional network context (e.g. 5G, Wi-Fi, datacenter).

FAQ

Frequently asked questions

What does "dynamic cipher tuning" actually do?

It picks cipher, chunk size, and entropy source for each stream based on workload profile and observed performance. Instead of one static configuration, the agent adapts to the codec, bitrate, and latency target you actually care about.

How does it keep latency low for real-time and streaming workloads?

By sizing chunks to the codec's natural frame boundary, pre-warming entropy buffers, and choosing GCM modes that pipeline well on the host CPU. The result is consistent encryption overhead well within real-time and live streaming budgets.

Does optimizing for speed reduce security?

No. The agent only chooses between configurations that meet AKRUM's security floor — AES-GCM family, keys derived from the Cellular Automata entropy engine mixed with the OS CSPRNG, modern TLS. Optimization happens inside that safe envelope, so you trade off implementation cost, not cryptographic strength.

Which streaming protocols and workloads are supported?

HLS, DASH, WebRTC media flows, low-latency live streams, and high-throughput batch pipelines that look like streams to the network. Anything that ingests chunked data over TLS can be tuned by the agent through its REST endpoint.

Is there a free sandbox to test it?

Yes. The sandbox lets you describe a workload — codec, bitrate, latency target — and see the agent's recommended cipher, chunk size, and entropy source plus the expected overhead. Free covers exploration before you ship a production key.

Ready to integrate?

Get an API key and start calling AI Encryption Optimization Agent in minutes.

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