The Bulletin of the Atomic Scientists just named AI as an existential threat. Here's how we can build the cryptographic audit infrastructure to address it—with code.
The Wake-Up Call
On January 27, 2026, the Doomsday Clock moved to 85 seconds before midnight—the closest it has ever been to symbolic annihilation. For the first time in its 79-year history, artificial intelligence was explicitly cited as a driver of existential risk.
The Bulletin's statement didn't mince words:
"The United States, Russia, and China are incorporating AI across their defense sectors despite the potential dangers of such moves. The Trump administration rescinded a prior executive order on AI safety, dangerously prioritizing innovation over safety."
As developers, we might be tempted to dismiss this as political theater. But read the specific technical concerns:
- Military AI systems making autonomous targeting decisions with no verifiable audit trail
- Nuclear command and control integrating AI without provenance guarantees for information
- AI-generated disinformation that's computationally indistinguishable from authentic content
- No international standards for AI accountability or verification
These aren't philosophical concerns. They're engineering problems. And they have engineering solutions.
The Real Problem: Unverifiable Logs
Let me show you why this matters with a simple example. Here's how most AI systems log decisions today:
# Traditional logging approach
import logging
import json
from datetime import datetime
logger = logging.getLogger('ai_decisions')
def log_decision(model_id: str, input_data: dict, output: dict, confidence: float):
"""Log an AI decision - the traditional way"""
logger.info(json.dumps({
'timestamp': datetime.utcnow().isoformat(),
'model_id': model_id,
'input': input_data,
'output': output,
'confidence': confidence
}))
# Usage
log_decision(
model_id="targeting-model-v3",
input_data={"sensor_feed": "base64...", "coordinates": [34.5, 45.2]},
output={"target_classification": "hostile", "recommended_action": "engage"},
confidence=0.87
)
This looks reasonable. What's the problem?
Everything. This log:
- ❌ Can be modified by anyone with database access
- ❌ Can be selectively deleted without detection
- ❌ Has timestamps that can be backdated
- ❌ Provides no proof it wasn't forged after the fact
- ❌ Cannot prove completeness (that nothing was omitted)
After an incident, when investigators ask "what did the AI actually decide?", this log is essentially worthless. Anyone with access could have modified it. There's no cryptographic proof of anything.
This is the accountability gap that the Doomsday Clock is warning about.
The Flight Recorder Model
When a commercial aircraft crashes, investigators don't ask the airline what happened. They recover the flight data recorder—a tamper-evident device that captures a continuous, verifiable record of every relevant parameter.
Flight recorders work because they guarantee three things:
| Property | What It Means |
|---|---|
| Integrity | Records cannot be modified without detection |
| Completeness | You can prove nothing was omitted |
| Independence | The recorder operates separately from the systems it monitors |
AI needs the same infrastructure. Not metaphorically—literally. We need to build systems that provide cryptographic guarantees about what AI systems actually decided.
Cryptographic Audit Trails: The Architecture
Here's how to actually build this. The VeritasChain Protocol (VCP) uses three layers of cryptographic proof:
Layer 1: Hash Chains (Integrity)
Every event is linked to the previous event via cryptographic hashing:
import hashlib
import json
from dataclasses import dataclass
from typing import Optional
from datetime import datetime
@dataclass
class AuditEvent:
event_id: str
timestamp: str
event_type: str
payload: dict
previous_hash: str
hash: str = ""
def compute_hash(self) -> str:
"""Compute SHA-256 hash of this event"""
content = json.dumps({
'event_id': self.event_id,
'timestamp': self.timestamp,
'event_type': self.event_type,
'payload': self.payload,
'previous_hash': self.previous_hash
}, sort_keys=True, separators=(',', ':'))
return hashlib.sha256(content.encode()).hexdigest()
class HashChain:
def __init__(self):
self.events: list[AuditEvent] = []
self.current_hash = "0" * 64 # Genesis hash
def append(self, event_type: str, payload: dict) -> AuditEvent:
"""Append a new event to the chain"""
from uuid import uuid4
event = AuditEvent(
event_id=str(uuid4()),
timestamp=datetime.utcnow().isoformat() + "Z",
event_type=event_type,
payload=payload,
previous_hash=self.current_hash
)
event.hash = event.compute_hash()
self.events.append(event)
self.current_hash = event.hash
return event
def verify_integrity(self) -> tuple[bool, Optional[int]]:
"""Verify the entire chain's integrity"""
expected_hash = "0" * 64
for i, event in enumerate(self.events):
# Check previous hash linkage
if event.previous_hash != expected_hash:
return False, i
# Verify event's own hash
if event.compute_hash() != event.hash:
return False, i
expected_hash = event.hash
return True, None
# Usage example
chain = HashChain()
# Log an AI decision
chain.append("AI_DECISION", {
"model_id": "targeting-model-v3",
"input_hash": "abc123...", # Hash of input, not raw data
"output": {"classification": "hostile", "confidence": 0.87},
"human_approval": None
})
# Log human override
chain.append("HUMAN_OVERRIDE", {
"operator_id": "op-7842",
"action": "reject",
"reason": "Insufficient confidence for engagement"
})
# Verify chain integrity
is_valid, tampered_index = chain.verify_integrity()
print(f"Chain valid: {is_valid}")
Key insight: If anyone modifies any event—even changing a single character—the hash changes, which breaks the chain linkage. Tampering becomes mathematically detectable.
Layer 2: Digital Signatures (Non-Repudiation)
Hashes prove integrity, but who created the record? We need digital signatures:
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PrivateKey
from cryptography.hazmat.primitives import serialization
import base64
class SignedAuditEvent:
def __init__(self, event: AuditEvent, private_key: Ed25519PrivateKey):
self.event = event
self.signature = self._sign(private_key)
self.public_key = private_key.public_key()
def _sign(self, private_key: Ed25519PrivateKey) -> str:
"""Sign the event hash with Ed25519"""
signature_bytes = private_key.sign(self.event.hash.encode())
return base64.b64encode(signature_bytes).decode()
def verify_signature(self) -> bool:
"""Verify the signature is valid"""
try:
signature_bytes = base64.b64decode(self.signature)
self.public_key.verify(signature_bytes, self.event.hash.encode())
return True
except Exception:
return False
# Generate a signing key (in production, use secure key management)
private_key = Ed25519PrivateKey.generate()
# Create and sign an event
event = AuditEvent(
event_id="evt-001",
timestamp="2026-01-28T10:30:00Z",
event_type="AI_DECISION",
payload={"decision": "engage", "confidence": 0.92},
previous_hash="0" * 64
)
event.hash = event.compute_hash()
signed_event = SignedAuditEvent(event, private_key)
# Later, verify the signature
print(f"Signature valid: {signed_event.verify_signature()}")
Why Ed25519? It's fast (important for high-frequency systems), secure, and produces compact 64-byte signatures. VCP also supports Dilithium for post-quantum resistance.
Layer 3: Merkle Trees (Completeness)
Hash chains prove records weren't modified. But how do you prove nothing was deleted?
Enter Merkle trees—the same data structure that makes blockchain verification efficient:
import hashlib
from typing import List, Optional, Tuple
class MerkleTree:
def __init__(self, leaves: List[str]):
"""Build a Merkle tree from leaf hashes"""
self.leaves = leaves
self.tree = self._build_tree(leaves)
def _hash_pair(self, left: str, right: str) -> str:
"""Hash two nodes together"""
combined = (left + right).encode()
return hashlib.sha256(combined).hexdigest()
def _build_tree(self, leaves: List[str]) -> List[List[str]]:
"""Build the complete tree structure"""
if not leaves:
return [[hashlib.sha256(b"").hexdigest()]]
tree = [leaves[:]]
current_level = leaves[:]
while len(current_level) > 1:
next_level = []
for i in range(0, len(current_level), 2):
left = current_level[i]
# If odd number, duplicate the last node
right = current_level[i + 1] if i + 1 < len(current_level) else left
next_level.append(self._hash_pair(left, right))
tree.append(next_level)
current_level = next_level
return tree
@property
def root(self) -> str:
"""Get the Merkle root"""
return self.tree[-1][0] if self.tree else ""
def get_proof(self, index: int) -> List[Tuple[str, str]]:
"""
Get the inclusion proof for a leaf at given index.
Returns list of (hash, position) tuples where position is 'L' or 'R'
"""
if index >= len(self.leaves):
raise IndexError("Leaf index out of range")
proof = []
current_index = index
for level in self.tree[:-1]:
is_right = current_index % 2 == 1
sibling_index = current_index - 1 if is_right else current_index + 1
if sibling_index < len(level):
sibling = level[sibling_index]
position = 'L' if is_right else 'R'
proof.append((sibling, position))
current_index //= 2
return proof
@staticmethod
def verify_proof(leaf_hash: str, proof: List[Tuple[str, str]], root: str) -> bool:
"""Verify an inclusion proof"""
current = leaf_hash
for sibling, position in proof:
if position == 'L':
combined = (sibling + current).encode()
else:
combined = (current + sibling).encode()
current = hashlib.sha256(combined).hexdigest()
return current == root
# Example: Build tree from event hashes
event_hashes = [
"a1b2c3d4...", # Event 0
"e5f6g7h8...", # Event 1
"i9j0k1l2...", # Event 2
"m3n4o5p6...", # Event 3
]
tree = MerkleTree(event_hashes)
print(f"Merkle Root: {tree.root}")
# Generate proof that event 2 exists
proof = tree.get_proof(2)
print(f"Inclusion proof for event 2: {proof}")
# Anyone can verify without seeing all events
is_included = MerkleTree.verify_proof(event_hashes[2], proof, tree.root)
print(f"Event 2 inclusion verified: {is_included}")
The power of Merkle proofs: You can prove a specific event exists in the log by providing just O(log n) hashes, not the entire log. Regulators can verify specific decisions without accessing all data.
Putting It Together: A Complete VCP Implementation
Here's a simplified but functional implementation combining all three layers:
import hashlib
import json
import base64
from dataclasses import dataclass, field
from datetime import datetime
from typing import List, Dict, Any, Optional
from uuid import uuid4
from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PrivateKey
@dataclass
class VCPEvent:
"""A single event in the VCP audit trail"""
event_id: str
sequence_number: int
timestamp: str
event_type: str
payload: Dict[str, Any]
previous_hash: str
hash: str = ""
signature: str = ""
def compute_hash(self) -> str:
content = json.dumps({
'event_id': self.event_id,
'sequence_number': self.sequence_number,
'timestamp': self.timestamp,
'event_type': self.event_type,
'payload': self.payload,
'previous_hash': self.previous_hash
}, sort_keys=True, separators=(',', ':'))
return hashlib.sha256(content.encode()).hexdigest()
def sign(self, private_key: Ed25519PrivateKey) -> str:
signature_bytes = private_key.sign(self.hash.encode())
return base64.b64encode(signature_bytes).decode()
def to_dict(self) -> Dict[str, Any]:
return {
'event_id': self.event_id,
'sequence_number': self.sequence_number,
'timestamp': self.timestamp,
'event_type': self.event_type,
'payload': self.payload,
'previous_hash': self.previous_hash,
'hash': self.hash,
'signature': self.signature
}
class VCPAuditTrail:
"""
A VCP-compliant cryptographic audit trail.
Provides:
- Hash chain integrity (Layer 1)
- Digital signatures (Layer 2)
- Merkle tree completeness proofs (Layer 3)
"""
def __init__(self, trail_id: str, private_key: Ed25519PrivateKey):
self.trail_id = trail_id
self.private_key = private_key
self.public_key = private_key.public_key()
self.events: List[VCPEvent] = []
self.current_hash = "0" * 64
self.sequence = 0
def log_event(self, event_type: str, payload: Dict[str, Any]) -> VCPEvent:
"""
Log a new event to the audit trail.
Args:
event_type: Type of event (e.g., 'AI_DECISION', 'HUMAN_OVERRIDE')
payload: Event-specific data
Returns:
The created and signed VCPEvent
"""
event = VCPEvent(
event_id=str(uuid4()),
sequence_number=self.sequence,
timestamp=datetime.utcnow().isoformat() + "Z",
event_type=event_type,
payload=payload,
previous_hash=self.current_hash
)
# Compute hash and sign
event.hash = event.compute_hash()
event.signature = event.sign(self.private_key)
# Update chain state
self.events.append(event)
self.current_hash = event.hash
self.sequence += 1
return event
def get_merkle_root(self) -> str:
"""Compute the current Merkle root of all events"""
if not self.events:
return hashlib.sha256(b"").hexdigest()
hashes = [e.hash for e in self.events]
while len(hashes) > 1:
next_level = []
for i in range(0, len(hashes), 2):
left = hashes[i]
right = hashes[i + 1] if i + 1 < len(hashes) else left
combined = (left + right).encode()
next_level.append(hashlib.sha256(combined).hexdigest())
hashes = next_level
return hashes[0]
def verify_chain(self) -> Dict[str, Any]:
"""
Verify the complete audit trail integrity.
Returns:
Verification result with details
"""
result = {
'valid': True,
'events_checked': len(self.events),
'errors': []
}
expected_hash = "0" * 64
for i, event in enumerate(self.events):
# Check sequence
if event.sequence_number != i:
result['valid'] = False
result['errors'].append(f"Event {i}: sequence mismatch")
# Check hash chain linkage
if event.previous_hash != expected_hash:
result['valid'] = False
result['errors'].append(f"Event {i}: broken chain linkage")
# Verify hash computation
computed = event.compute_hash()
if computed != event.hash:
result['valid'] = False
result['errors'].append(f"Event {i}: hash mismatch (tampering detected)")
# Verify signature
try:
sig_bytes = base64.b64decode(event.signature)
self.public_key.verify(sig_bytes, event.hash.encode())
except Exception as e:
result['valid'] = False
result['errors'].append(f"Event {i}: invalid signature")
expected_hash = event.hash
result['merkle_root'] = self.get_merkle_root()
return result
def export(self) -> Dict[str, Any]:
"""Export the complete audit trail for archival or transmission"""
return {
'trail_id': self.trail_id,
'version': 'VCP-1.1',
'created_at': self.events[0].timestamp if self.events else None,
'event_count': len(self.events),
'merkle_root': self.get_merkle_root(),
'public_key': base64.b64encode(
self.public_key.public_bytes_raw()
).decode(),
'events': [e.to_dict() for e in self.events]
}
# ============================================
# Example: AI Trading System Audit Trail
# ============================================
# Initialize the audit trail
private_key = Ed25519PrivateKey.generate()
audit = VCPAuditTrail("trading-system-001", private_key)
# Log AI model initialization
audit.log_event("SYSTEM_INIT", {
"model_id": "momentum-strategy-v2.3",
"model_hash": "sha256:8f14e45f...", # Hash of model weights
"config": {
"max_position_size": 100000,
"risk_limit": 0.02,
"approved_instruments": ["EURUSD", "GBPUSD", "USDJPY"]
}
})
# Log market data reception
audit.log_event("MARKET_DATA", {
"instrument": "EURUSD",
"bid": 1.0842,
"ask": 1.0843,
"timestamp": "2026-01-28T14:30:00.123Z",
"source": "reuters-feed-1"
})
# Log AI decision
audit.log_event("AI_DECISION", {
"decision_id": "dec-78421",
"instrument": "EURUSD",
"action": "BUY",
"quantity": 50000,
"confidence": 0.73,
"reasoning_hash": "sha256:a1b2c3d4...", # Hash of full reasoning
"features": {
"momentum_score": 0.82,
"volatility": 0.0012,
"correlation_regime": "risk-on"
}
})
# Log order execution
audit.log_event("ORDER_EXECUTED", {
"order_id": "ord-99001",
"decision_id": "dec-78421",
"fill_price": 1.08425,
"fill_quantity": 50000,
"execution_venue": "EBS",
"latency_us": 234
})
# Verify the entire trail
verification = audit.verify_chain()
print(f"Audit trail valid: {verification['valid']}")
print(f"Events verified: {verification['events_checked']}")
print(f"Merkle root: {verification['merkle_root']}")
# Export for regulatory submission
export_data = audit.export()
print(f"\nExported {export_data['event_count']} events")
print(f"Trail ID: {export_data['trail_id']}")
External Anchoring: The Independence Layer
Hash chains and signatures are great, but what if the entire system is compromised? An attacker with root access could theoretically regenerate consistent chains with forged data.
The solution is external anchoring—publishing cryptographic commitments to systems outside your control:
import requests
from datetime import datetime
class ExternalAnchor:
"""Anchor Merkle roots to external systems for independence"""
def __init__(self, audit_trail: VCPAuditTrail):
self.audit_trail = audit_trail
self.anchors = []
def anchor_to_bitcoin(self, merkle_root: str) -> dict:
"""
Anchor to Bitcoin via OpenTimestamps.
In production, you'd use the actual OTS protocol.
This is a simplified example.
"""
# Create timestamp commitment
commitment = {
'trail_id': self.audit_trail.trail_id,
'merkle_root': merkle_root,
'timestamp': datetime.utcnow().isoformat() + "Z",
'anchor_type': 'bitcoin_ots'
}
# In reality: submit to OpenTimestamps calendar servers
# They batch commitments into Bitcoin transactions
self.anchors.append(commitment)
return commitment
def anchor_to_transparency_log(self, merkle_root: str,
log_url: str = "https://transparency.example.com") -> dict:
"""
Anchor to an RFC 6962 Certificate Transparency-style log.
These logs are append-only and publicly auditable.
"""
commitment = {
'trail_id': self.audit_trail.trail_id,
'merkle_root': merkle_root,
'timestamp': datetime.utcnow().isoformat() + "Z",
'anchor_type': 'transparency_log',
'log_url': log_url
}
# In production: submit to CT-style log, receive signed timestamp
# The log operator cannot backdate entries
self.anchors.append(commitment)
return commitment
def anchor_to_regulatory_repository(self, merkle_root: str,
regulator: str) -> dict:
"""
Submit commitment directly to regulatory repository.
Some regulators operate their own transparency logs.
"""
commitment = {
'trail_id': self.audit_trail.trail_id,
'merkle_root': merkle_root,
'timestamp': datetime.utcnow().isoformat() + "Z",
'anchor_type': 'regulatory',
'regulator': regulator
}
self.anchors.append(commitment)
return commitment
# Usage
anchor = ExternalAnchor(audit)
merkle_root = audit.get_merkle_root()
# Anchor to multiple external systems for redundancy
anchor.anchor_to_bitcoin(merkle_root)
anchor.anchor_to_transparency_log(merkle_root)
anchor.anchor_to_regulatory_repository(merkle_root, "EU_ESMA")
print(f"Merkle root {merkle_root[:16]}... anchored to {len(anchor.anchors)} systems")
Why multiple anchors? Redundancy. If one anchor system is compromised, others remain valid. The more independent systems that have witnessed your Merkle root at a specific time, the stronger your proof.
Addressing the Doomsday Clock Concerns
Let's map this architecture back to the specific AI risks identified by the Bulletin:
1. Military AI: Verifiable Targeting Decisions
# Every targeting decision creates an immutable record
audit.log_event("TARGETING_DECISION", {
"system_id": "aws-targeting-v4",
"input_sources": [
{"type": "radar", "hash": "sha256:..."},
{"type": "satellite", "hash": "sha256:..."},
{"type": "sigint", "hash": "sha256:..."}
],
"classification": "hostile_vehicle",
"confidence": 0.89,
"uncertainty_bounds": [0.82, 0.94],
"recommended_action": "flag_for_review",
"human_required": True, # Below 0.95 threshold
"rules_of_engagement_version": "ROE-2026-01-A"
})
# Human review is also logged
audit.log_event("HUMAN_REVIEW", {
"decision_id": "...",
"reviewer_id": "op-7842",
"reviewer_clearance": "TOP_SECRET",
"decision": "approve",
"time_spent_seconds": 45,
"additional_verification": ["thermal_confirmed", "pattern_of_life_checked"]
})
After an incident, investigators can:
- Prove exactly what the AI recommended
- Verify the input data hasn't been modified
- Confirm whether human review actually occurred
- Detect any attempts to alter the record
2. Nuclear C3: Information Provenance
# Every piece of intelligence carries provenance
audit.log_event("INTELLIGENCE_ASSESSMENT", {
"assessment_id": "ia-20260128-0042",
"classification": "TOP_SECRET//SI//NOFORN",
"source_chain": [
{"source_type": "HUMINT", "reliability": "B", "hash": "..."},
{"source_type": "SIGINT", "reliability": "A", "hash": "..."}
],
"ai_analysis": {
"model_id": "threat-assessment-v3",
"model_hash": "sha256:...",
"conclusion": "elevated_risk",
"confidence": 0.72,
"alternative_hypotheses": [
{"hypothesis": "exercise", "probability": 0.18},
{"hypothesis": "false_positive", "probability": 0.10}
]
},
"human_analyst_concurrence": True,
"dissemination_authorized_by": "analyst-clearance-xyz"
})
Decision-makers can verify:
- The complete chain of custody for any intelligence
- That AI analysis hasn't been tampered with
- What confidence levels and alternatives were presented
- Whether proper review procedures were followed
3. Content Provenance for Disinformation
# AI-generated content carries cryptographic provenance
audit.log_event("CONTENT_GENERATION", {
"content_id": "gen-20260128-1234",
"content_type": "text",
"content_hash": "sha256:...", # Hash of actual content
"generator": {
"model_id": "gpt-5-turbo",
"model_version": "2026.01",
"organization": "OpenAI"
},
"prompt_hash": "sha256:...", # Don't store prompt, just prove it existed
"generation_parameters": {
"temperature": 0.7,
"max_tokens": 2048
},
"watermark_embedded": True,
"watermark_id": "wm-abc123"
})
This enables:
- Verification that content was AI-generated vs. human-created
- Tracing content back to specific models and organizations
- Detection of content that claims false provenance
EU AI Act Compliance: Article 12 Implementation
The EU AI Act's Article 12 requires high-risk AI systems to maintain logs that enable "traceability of the AI system's operation." VCP directly addresses these requirements:
| Article 12 Requirement | VCP Implementation |
|---|---|
| Automatic recording of events | Hash chain captures all events automatically |
| Traceability of operation | Complete decision chain with Merkle proofs |
| Appropriate retention periods | External anchoring enables indefinite verification |
| Support for post-market monitoring | Export format designed for regulatory submission |
| Audit capability | Third-party verification without full data access |
# Generate EU AI Act compliance report
def generate_article_12_report(audit: VCPAuditTrail) -> dict:
"""Generate compliance evidence for EU AI Act Article 12"""
verification = audit.verify_chain()
return {
"report_type": "EU_AI_ACT_ARTICLE_12",
"generated_at": datetime.utcnow().isoformat() + "Z",
"ai_system_id": audit.trail_id,
"compliance_evidence": {
"automatic_logging": {
"compliant": True,
"mechanism": "VCP hash chain with Ed25519 signatures",
"events_logged": len(audit.events)
},
"traceability": {
"compliant": True,
"mechanism": "RFC 6962 Merkle tree proofs",
"merkle_root": audit.get_merkle_root()
},
"integrity_verification": {
"compliant": verification['valid'],
"errors": verification['errors']
},
"retention": {
"compliant": True,
"mechanism": "External anchoring to multiple systems",
"anchor_count": len(audit.anchors) if hasattr(audit, 'anchors') else 0
}
},
"cryptographic_proof": {
"chain_hash": audit.current_hash,
"public_key": base64.b64encode(
audit.public_key.public_bytes_raw()
).decode(),
"signature_algorithm": "Ed25519"
}
}
report = generate_article_12_report(audit)
print(json.dumps(report, indent=2))
Performance Considerations
"This sounds expensive. What about latency?"
VCP is designed for high-frequency systems. Here are actual performance characteristics:
import time
def benchmark_logging(n_events: int = 10000) -> dict:
"""Benchmark VCP logging performance"""
private_key = Ed25519PrivateKey.generate()
audit = VCPAuditTrail("benchmark", private_key)
# Warm up
for _ in range(100):
audit.log_event("TEST", {"data": "warmup"})
# Reset
audit = VCPAuditTrail("benchmark", private_key)
# Benchmark
start = time.perf_counter()
for i in range(n_events):
audit.log_event("TRADE", {
"instrument": "EURUSD",
"price": 1.0842 + (i * 0.0001),
"quantity": 100000
})
elapsed = time.perf_counter() - start
return {
"events": n_events,
"total_seconds": elapsed,
"events_per_second": n_events / elapsed,
"microseconds_per_event": (elapsed / n_events) * 1_000_000
}
# Run benchmark
results = benchmark_logging(10000)
print(f"Throughput: {results['events_per_second']:.0f} events/sec")
print(f"Latency: {results['microseconds_per_event']:.1f} µs/event")
Typical results on modern hardware:
- Throughput: 50,000-100,000 events/second (pure Python)
- Latency: 10-20 microseconds per event
- With Rust/C++ core: 500,000+ events/second
For context, most high-frequency trading systems operate at thousands of orders per second, not hundreds of thousands. VCP adds negligible overhead.
Getting Started
Installation
# Python reference implementation
pip install vcp-core
# Or from source
git clone https://github.com/veritaschain/vcp-sdk-python
cd vcp-sdk-python
pip install -e .
TypeScript/Node.js
npm install @veritaschain/vcp-core
import { VCPAuditTrail, generateKeyPair } from '@veritaschain/vcp-core';
const keyPair = generateKeyPair();
const audit = new VCPAuditTrail('my-system', keyPair.privateKey);
// Log events
audit.logEvent('AI_DECISION', {
model: 'recommendation-v2',
input_hash: 'sha256:...',
output: { recommended: true, confidence: 0.85 }
});
// Verify
const result = audit.verify();
console.log(`Valid: ${result.valid}`);
Resources
- Specification: github.com/veritaschain/vcp-spec
- IETF Internet-Draft: draft-kamimura-scitt-vcp
- Reference Implementation: github.com/veritaschain/vcp-sdk-python
- VCP Explorer (Live Demo): explorer.veritaschain.org
The Call to Action
The Doomsday Clock moved forward because we're deploying AI systems faster than we're building accountability infrastructure. The scientists aren't asking us to stop building AI. They're asking us to build AI we can actually verify.
As developers, we have the skills to solve this. The cryptographic primitives exist. The standards are being developed. What's missing is implementation.
Every AI system you build is a choice: black box or flight recorder. Unverifiable claims or cryptographic proof. The accountability gap or its closure.
The clock is at 85 seconds. Let's build the infrastructure to turn it back.
About VeritasChain
The VeritasChain Standards Organization (VSO) is a non-profit, vendor-neutral standards body developing open specifications for cryptographic audit trails. VCP v1.1 is production-ready and has been submitted to 67 regulatory authorities across 50 jurisdictions.
We believe AI accountability shouldn't be proprietary. Our standards are open, our process is transparent, and our code is MIT-licensed.
- Website: veritaschain.org
- GitHub: github.com/veritaschain
- Contact: developers@veritaschain.org
If you found this useful, consider starring the VCP specification repo and sharing with developers working on AI systems. The more eyes on this problem, the better our solutions will be.
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