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Artificial Intelligence Security

AI/ML Security Challenges

CISA and international partners released joint guidance on AI Data Security best practices in May 2025, highlighting critical risks across the AI lifecycle.

Key Security Concerns:

• Data Poisoning: Malicious training data injection
• Model Inversion: Extracting training data from models
• Adversarial Examples: Inputs designed to fool AI systems
• Prompt Injection: Manipulating AI system inputs
• Model Theft: Unauthorized model replication

AI Security Best Practices

• Secure AI Development: Security-by-design principles
• Data Protection: Training data classification and access controls
• Model Validation: Adversarial testing and validation
• Runtime Protection: Input validation and output filtering
• Monitoring: AI system behavior analysis

Cloud Security

Shared Responsibility Model

• Cloud Provider: Physical security, infrastructure, platform services
• Customer: Data, identity, applications, network controls, operating system

Cloud Security Challenges

• Visibility: Limited insight into cloud infrastructure
• Compliance: Meeting regulatory requirements in cloud
• Data Location: Geographic and jurisdictional considerations
• Identity Management: Federated identity and access management
• Configuration: Secure cloud service configuration

Cloud Security Tools

• Cloud Security Posture Management (CSPM): Configuration assessment
• Cloud Workload Protection Platform (CWPP): Runtime workload security
• Cloud Access Security Broker (CASB): Data protection and compliance
• Container Security: Image scanning, runtime protection
• Serverless Security: Function-level security controls

Internet of Things (IoT) Security

IoT Security Challenges

• Device Constraints: Limited processing power and memory
• Update Management: Difficult firmware patching
• Default Credentials: Weak or unchanged default passwords
• Network Exposure: Direct internet connectivity
• Device Lifecycle: Long deployment periods with minimal maintenance

NIST IoT Security Framework

NIST continues to develop IoT cybersecurity guidance, with foundational activities including:

• Device Identification: Asset inventory and management
• Device Configuration: Secure initial setup
• Data Protection: Encryption and access controls
• Interface Security: Secure communications
• Software Updates: Secure update mechanisms
• Cybersecurity State Awareness: Monitoring and logging

Blockchain and Distributed Systems

Smart Contract Security (OWASP Smart Contract Top 10 2025)

1. Access Control Vulnerabilities: Poorly implemented permissions
2. Arithmetic Issues: Integer overflow/underflow
3. Unchecked External Calls: Reentrancy attacks
4. Lack of Input Validation: Unvalidated user inputs
5. Reentrancy Attacks: Callback exploitation
6. Gas Limit Vulnerabilities: Resource exhaustion
7. Weak Randomness: Predictable random number generation
8. Privacy Issues: On-chain data exposure
9. Logic Issues: Smart contract business logic flaws
10. Denial of Service: Contract unavailability attacks

Blockchain Security Considerations

• Consensus Mechanisms: Proof-of-Work vs. Proof-of-Stake security
• Key Management: Private key security and recovery
• Smart Contract Auditing: Code review and formal verification
• Network Security: Node protection and communication security

Quantum Computing Implications

Post-Quantum Cryptography

• Timeline: NIST standardization in progress
• Impact: Current encryption algorithms vulnerable
• Migration Strategy: Hybrid classical-quantum resistant systems
• Standards:
  • NIST SP 800-208: Recommendation for Stateful Hash-Based Signature Schemes
  • NIST SP 800-232: Ascon-Based Lightweight Cryptography (released 2025)

Quantum-Safe Algorithms

• Key Exchange: CRYSTALS-Kyber
• Digital Signatures: CRYSTALS-Dilithium, FALCON, SPHINCS+
• Hash-Based Signatures: XMSS, LMS
• Implementation: Gradual transition and testing

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