The widespread adoption of Artificial Intelligence (AI) Applications and services is driving a fundamental shift in how Chief Information Security Officer (CISOS) Structure his Cyber Security Policies and Strategies.
The unique characteristics of Ai, its data-intenseve nature, complex models, and potential for autonomous decision-making into new attacques and risks that neccaIDIETE and SACSIKS Policy Enhancements and Strategic Recalibrations.
The primary goals are to preventing inadvertent data leakage by employees using ai and Generative Ai (Genai) tools and to ensure that decisions based on ai systems are not compromised by MALICIOUS Asters, Whather Internal or External. Below is a strategic blueprint for cisos to align cybersecurity with the secure deployment and use of genai systems.
- Revamp Acceptable Use and Data Handling Policies for Ai: Existing acceptable use policies (Aups) must be updated specifically to address the use of Ai tools, explicitly prohibiting the input of sensitive, confidential, or proprietary data into public or foundo. Sensitive Data Could Include Customer Personal Information, Financial Records or Trade Secrets. Policies should clearly define what constituted 'sensitive' data in the context of ai. Data Handling Policies must also Detail requirements for anonymisation, pseudonyMization, and tokenisation of data used for internal ai model training or fin-tuning.
- Mitigate AI System Compromise and Tampering: CISOS MUST FOCUS on AI System Integrity and Security. Deploy Security Practices Into The Entre Ai Development Pipeline, from Secure Coding for Ai Models to Rigorous Testing for Vulnerabilites Like Prompt InjectionData Poisoning and model inversionImplement Strong Filters and Validators for all data entering the AI System (Prompts, Retrieved Data for Rag) to Prevent Adversarial Attacks. Similarly, all ai-generated outputs must be sanitised and validated before being presented to users or used in Downstream Systems to Avoid Malicious Injections. Wherever feasible, Deploy AI Systems with Xai Capability, Allowing for Transparency Into How Decisions Are Made. For High-Stakes Decisions, Mandate Human Evercest When Handling Sensitive Data or Performing Irreversible Operations to Provide a Final Safeguard Against Compromise ai output.
- Building resilient and secure ai development pipelines: Securing ai development pipelines Is paramount to ensuring the Trustworthiness and Resilience of AI Applications Integrated Into Critical Network Infrastructure, Security Products and Collaborate Solutions. It Necessmittes Embedding Security through the Entire Ai Lifecycle. Genai Code, Models and Training Datasets are part of the modern software supply chain. Secure aiops pipelines with CI/CD Best PracticesCode signing and model integrity checks. Scan training datasets and model artifacts for malicious code or trojanede weights. Vet Third-Parthy Models and Libraries for Backdoors and License Compliance.
- Implement a complete ai governance framework: Cisos must champion the creation of an enterprise-wide ai governance framework that embeds security from the outset. AI Risks should not be islated but woven into enterprise-wide risk management and compliance practices. This Framework should define explicit roles and responsibilities for ai development, deployment and oversight to establish an ai-teenric risk management process. A centralized inventory of approved ai tools should be maintained, along with their risk classifications. The Governance Framework Helps Substantily in Managing The Risk Associated with “Shadow Ai”, the use of unsancing ai tools or services. Mandate only approved ai tools and block all other AI tools and services.
- Strengthen data loss prevention tools (dlps) for ai workflows: DLP strategies MUST Evolve to Detect and Prevent Sensitive Data from Enown Unauthorized AI Environments or Being Exfiltled Via Ai Outputs. This involves configuring dlp tools to specificly monitor ai interaction channels (EG chat interfaces and api calls to llms), identifying patterns indicative of sensitive data being input. AI-Specific DLP Rules must be developed to block or flag attempts to paste pii, Intellectual Property or Confidential Code into Public Ai Prompts.
- Enhance Employee and Leadership Ai Awareness Training: Employees are often the weakest link in the Organization. CISOS MUST Implement Targeted, Continuous Training Programmes On the Acceptable Use of Ai, Identtify AI-Tentric Threats, Promote Engineering Best Practices, and Provide education on reporting Incidents related to the misuse of ai tools and potential compromise.
- Institute Vendor Risk Management for Ai Services: As companies increase relay on third-party ai services, cisos must enjoy their Third-party risk management (TPRM) Programmes to Address These Risks. They should define standards for assessing the security posture of the AI vendor's supply chain, adhering to robust contractual clauses that mandate security standards, data private privacy, liability for generality for generality forms, and Audit Rights for Ai Service Providers. There should be in-depth security assessments of AI vendors, scrutinizing their data handling practices, model security, api security, and ai-specification responsible responses.
- Integrate Continual Monitoring and Adversarial Testing: In the Ever-Evolving Landscape of Ai Threats and Risks, Static Security Measures are Insufficient. Cisos should stress the importance of Continual monitoring of ai systems to detect potential compromises, data leaks or adversarial attackers – Signalled by unuspect patterns, UNESPECTED Outputs or sudden changes in model behavior. Regular Red Teaming and Adversarial Testing Exercises, Specifically Designed to Explit AI Vulnerabilites Should Help Organisations to Spot Weakneses Before Malicious ACTRES.
Cisos who make these changes will be better to manage the risks associateed with ai, enabling security practices to keep pace with or get ahead of ahead of ai deployment. This requires a shift from reactive defense to a proactive, adaptive security posure woven into the fabric of ai initiatives.
Aditya K Sood is Vice President of Security Engineering and AI Strategy at Aryaka,
Read more on Business Continuity Planning