AI trading security — a guide

Topic: Operational Resilience and Security in AI Trading

This document serves as a practical framework for CTOs and CISOs, aimed at establishing an operationally resilient security system for algorithmic trading. We move from general principles to concrete actions, metrics, and tools to minimize the risk of capital loss.

Key Priority Actions

For 30 Days (Foundation):

1. Asset Segregation: Conduct an audit and distribute capital across Cold/Warm/Hot storage according to a risk matrix.
2. Basic Multisig: Implement a "3-of-5" scheme for all critical wallets with clearly defined roles.
3. Incident Response Plan (IRP): Develop the first version of the IRP, including a playbook for key compromise, and define target metrics: MTTD < 1 hour and MTTR < 4 hours.

For 90 Days (Automation & Control):

1. DevSecOps: Integrate SAST/SCA scanners (Snyk, SonarQube) into CI/CD to block builds with critical vulnerabilities. Generate an SBOM for every release.
2. Logging & Monitoring: Configure collection of key events (Vault access, transactions >$10k, model deployment) into a SIEM, define retention policy (90 days hot, 1 year cold), and create alerting rules.
3. Threat Modeling: Conduct the first threat modeling session for key system components using the standard template (Likelihood × Impact).

For 180 Days (Maturity & Resilience):

1. Secret Centralization: Migrate 90% of all secrets to HashiCorp Vault with automatic rotation and RBAC policies.
2. ML Pipeline Protection: Implement model drift monitoring (KL, PSI) with threshold calibration based on historical data and automated retraining triggers.
3. Legal Protection: Secure a digital asset insurance policy and include specific SLAs and forensic support obligations in contracts with counterparties.

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1. Introduction: A Practical Framework for Asset Protection

Algorithmic trading using AI is not just a race for alpha, but a continuous effort to protect infrastructure. Success is defined by the reliability of a security system that meets industry standards such as the NIST Cybersecurity Framework (CSF) 2.0 and ISO/IEC 27001.

This guide offers a structured and operationally oriented framework for technical teams. Here you will find specific actions, templates, metrics, and trade-off discussions for building defense-in-depth: from key management and ML model security to incident response and legal aspects.

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2. Threat Landscape and Risk Modeling

2.1. Regulatory Risks: The MiCA Era and Global Oversight

• MiCA Regulation (Markets in Crypto-Assets) in the EU: Requires licensing, reserve requirements, and adherence to AML/CFT procedures. Non-compliance risks fines and license revocation.
• Global Trends: Regulators in the US (SEC, CFTC) are tightening control. Proactive implementation of AML procedures is a mandatory requirement for working with major exchanges and banks.

2.2. Systemic and Counterparty Risks

• Stablecoin Risks: Tether (USDT) carries asset freezing risks due to links with high-risk transactions (see UNODC report, Jan 2024).
• Centralized Platforms: When working with exchanges and custodians, conduct due diligence and require the inclusion of specific withdrawal SLAs in contracts.

2.3. Technological and Operational Vulnerabilities

Use the MITRE ATT&CK® (general tactics) and MITRE ATLAS™ (AI-specific) frameworks.

AI Model Risks (MLSecOps):

• Data Poisoning: Manipulation of training data to create backdoors.
• Adversarial Robustness: Inputting specifically altered data to cause incorrect predictions.
• Model Drift: Reduced accuracy due to changing market conditions.
• Supply Chain Vulnerabilities: Attacks via dependencies (e.g., compromised npm packages).

2.4. Threat Modeling

Use a risk assessment matrix (Likelihood × Impact).

Threat (MITRE ATLAS ID)	Attack Vector	Likelihood (1–5)	Impact (1–5)	Risk (L×I)	Mitigations	Owner
Data Poisoning (AML.T0002)	Compromise of S3 bucket with training data	3	5	15 (High)	1) Integrity control (hashing); 2) RBAC for S3; 3) Anomaly detectors.	CISO / ML Team
Key Compromise (ATT&CK T1552.004)	Exchange API key leak from CI/CD logs	4	5	20 (Critical)	1) Storage in Vault; 2) Auto-rotation; 3) IP whitelisting.	DevOps / Security
Dependency Vulnerability (ATT&CK T1195.001)	Use of vulnerable Python library	5	4	20 (Critical)	1) SCA scan (Snyk); 2) SBOM analysis; 3) Build blocking.	DevOps Team

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3. Practical Guide to Asset Protection

3.1. Asset Management: Capital Allocation Matrix

Company Profile	Liquidity Need	Risk Tolerance	Allocation (Cold / Warm / Hot)	Comment
HFT Fund	Very High	Medium	20–40% / 40–60% / 10–20%	Requires instant access to capital.
DeFi Yield Farming	High	High	40–60% / 30–40% / 10–20%	Constant movement of funds between protocols.
Long-term Fund (VC)	Low	Low	90–98% / 1–5% / 1–5%	Assets held for years, minimal operational liquidity.

3.2. Technical Defense Perimeter

Secure CI/CD (OWASP Top 10 CI/CD Security Risks):

• SCA (Software Composition Analysis): Integrate Snyk or OWASP Dependency-Check. Block builds with High or Critical vulnerabilities.
• SAST (Static Application Security Testing): Use SonarQube for code analysis.
• SBOM (Software Bill of Materials): Generate SBOM (using Syft) and check for new CVEs.
• Artifact Signing: Sign commits (GPG) and images (via Sigstore).

Secret Management:

• Centralization: HashiCorp Vault with RBAC policies (see Appendix A).
• Rotation: Automated key rotation (Target: every 90 days).
• Trade-offs (HSM vs. Custodian):
  • HSM: Maximum control, but complex to operate.
  • Custodian (Fireblocks, Copper): Insurance coverage, fast implementation, but reliance on a third party.

3.3. ML System Security (MLSecOps)

Data Integrity and Provenance:

• Versioning: Use DVC.
• Ingest Control: Hash checks, schema validation, anomaly detectors.

Model Integrity:

• Signing: Cryptographically sign models before deployment.
• Interpretability: Use SHAP/LIME and fallback strategies.

Monitoring and Robustness:

• Drift Control: KL (Kullback-Leibler) and PSI (Population Stability Index) metrics.
• Calibration: Calibrate thresholds (e.g., PSI > 0.25) based on historical data.
• Adversarial Tests: Regular testing via Adversarial Robustness Toolbox (ART).

3.4. Operational Readiness and Incident Response

Logging and Observability:

• What to Log: Vault access, model deploys, transactions >$10k, multisig changes.
• Retention: SIEM (Hot) — 90 days, S3 (Cold) — 1 year.
• Alert Examples:

ALERT IF: login_failures > 5 FROM one_ip IN 1_hour
ALERT IF: vault_secret_access BY non_whitelisted_service
ALERT IF: transaction_amount > $100k AND multisig_quorum_changed IN last_24_hours

Incident Response Plan (IRP):

• Metrics: MTTD < 1 hour, MTTR < 4 hours.
• Playbook Example (Key Compromise):
  1. Detection (T+0): SIEM alert.
  2. Containment (T+0–1h): Isolate systems, revoke keys, move funds to cold wallets.
  3. Escalation (T+1h): Assemble IRP team (CISO, CTO).
  4. Investigation: Log analysis, forensics.
  5. Recovery: New keys, backup restoration.

3.5. Legal Aspects

• SLA: Withdrawal < 24h, support response < 4h.
• Insurance: Analyze coverage (theft vs. smart contract bugs).

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4. Action Plan for 30/90/180 Days

First 30 Days: Foundation

1. Asset Audit: Report and allocation matrix.
2. Multisig: "3-of-5" scheme configured.
3. IRP: Approved v1 version.

First 90 Days: Automation

1. External Audit/Pentest: Report and remediation plan.
2. CI/CD Security: Build blocking on vulnerabilities, SBOM.
3. Logging: Basic alerts in SIEM.

First 180 Days: Maturity

1. Vault: 90% of secrets in Vault, auto-rotation for 50% of keys.
2. Insurance: Signed policy.
3. Drills: IRP testing.

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5. Maturity Metrics and KPIs

Domain	KPI	Target Value (1 Year)
Secret Management	% of secrets in Vault	> 90%
Secret Management	Average API key rotation time	< 90 days
DevSecOps	% of releases with SBOM	100%
DevSecOps	Time to remediate critical vulnerabilities	< 7 days
Incidents	IRP drill frequency	2 times/year
Incidents	MTTD / MTTR	< 1 hr / < 4 hrs
AML/Compliance	False positives in AML	< 5%