Securing AI
Artificial Intelligence (AI) adoption rate is a rapidly evolving and similarly the Cyber security risks for organizations
Integrate Governance and Security in to your AI adoption
Managing Risk in AI
Artificial Intelligence can optimize organizational operations and service or product offerings to meet emerging transformation requirements and maintain competitiveness. In order for organizations to deploy trustworthy artificial intelligence systems, they must identify and mitigate risks to prevent unintended consequences through sound security practices and verification, both infrastructural and AI/ML design implementations. Impacts of artificial intelligence are highly volatile, and the benefits and liabilities should be equally considered. Government and industry frameworks (i.e., NIST, ISO 42001, HITRUST) provide a foundation to help manage the associated risks and threats of artificial intelligence.
However, care should be given when adopting these frameworks to ensure that organizational priorities and objectives are properly aligned, and adequate testing and verification is conducted to ensure proper implementation of the control framework and alignment with societal norms, regulatory requirements and industry standards.
Integrating Secure AI in your organization
While there exist several frameworks and standards which can help organizations mitigate organizational risks, the risks posed by AI systems are in many ways unique including Privacy risks, data leakage, harmful bias or data quality to name a few, which can affect the trustworthiness of the AI system.
Privacy and Cybersecurity risk management considerations must be integrated in all phases of the lifecycle including design, development, deployment, evaluation, and use of AI systems. Furthermore, Privacy and Cybersecurity risks from AI must also be considered and integrated holistically in the organization's risk management strategy. As part of the effort to address AI trustworthiness characteristics such as “Secure and Resilient” and “Privacy-Enhanced,” organizations may consider leveraging available standards (i.e., NIST, ISO-42001, HITRUST) and guidance that aim in reducing security and privacy risks.
We work with your team to understand requirements and priorities, both tactical and strategic, and develop a roadmap to help you deploy and maintain AI systems that align with your organizational goals with clarity.
AI Security Lifecycle Activities
- Leadership; ensuring that the AI policy and AI objectives are clearly defined and properly align with the strategic direction of the organization
- Planning; articulate AI objectives and develop plan to achieve them along with addressing risks and opportunities (e.g., acceptable/non-acceptable risks, risk treatment)
- Implementation; security design, technical security requirements, system security lifecycle, security testing, awareness
- Operation; Operational planning and control definition, risk assessment, risk treatment and impact analysis
- Performance and Evaluation; Monitoring, measurement, analysis and evaluation
- Continual Improvement; Continually improve the suitability and effectiveness of the AI management system
Areas of Expertise
Governance
Enhance your existing Governance with the necessary elements to adopt AI uniformly across your organization (e.g., AI plans, AI system architecture, AI threats, purpose, appropriate uses, and inappropriate uses, expectations, positive and negative impacts, and appropriate data handling and storage).
Risk Management
- Identify, evaluate, quantify, and document AI system shortcomings through risk assessments, internal and external audits, and vulnerability scans.
- Communicate findings to stakeholders, both internal and external, to provide insight on AI system liabilities and intended improvements.
- Test, implement, and monitor AI system remedies in response to risk identification practices to determine security and operational effectiveness.
- Evaluate and communicate risk management strategies (e.g. risk tolerance, risk appetite, risk mitigation), findings, shortcomings, and improvements on a defined cadence
Security Testing and Verification
- Ensure implemented controls are operating as intended through quantitative and qualitative metrics. Document and communicate AI system shortcomings for additional analysis and remedies.
- Perform vulnerability assessments on both the AI system and the platform that is deployed, in order to identify and remediate gaps in the supporting software, plugins, and supporting infrastructure.
- Conduct penetration testing on AI implementations (i.e., Infrastructure and AI/ML Red Teaming) to assess AI/ML trustworthiness and determine areas for improvement. The penetration testing should take into consideration the following:
- Prompt Injection Attacks
- Evasion Attacks
- Reconstruction / Inference Attacks
- Sensitive Information Disclosure
- Supply Chain
- Data and Model Poisoning
- Improper Output Handling
- Excessive Agency
- System Prompt Leakage
- Vector and Embedding Weaknesses
- Misinformation
- Unbounded Consumption
Why Choose Palindrome Technologies?
Our team has been on the forefront of Cyber security since 1995 and supporting commercial and government organizations with securing high-assurance environments and emerging technologies. In addition to our extensive experience we participate in key industry initiatives to provide thought leadership and contributions to help improve Cyber security standards and frameworks, including FCC, NIST, ISACA, IEEE, GSMA, CTIA, and ISA standards.
Start Securing Your AI Journey
Leaving your mission-critical systems vulnerable can impact your reputation and cripple your market reach.
Palindrome Technologies can help you not only meet regulatory requirements but also demonstrate the highest levels of assurance to stakeholders and your commitment to keep your customers secure.
Learn more about AI Risk Management in our report