AI Security strategy and ethics

As AI-based solutions continue to transform the business landscape, it’s crucial to recognise that they are not infallible. In addition to being vulnerable to compromises like any other software, they may also magnify biases and cause unintended harm if not monitored effectively. These challenges pose new ethical, trust, risk, and security management requirements that traditional controls cannot address. This is also highlighted in the Market Guide for AI Trust, Risk, and Security Management released by Gartner®.

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SecAIS is keenly aware of the obstacles that businesses face when incorporating AI technology into their operations. We understand the criticality of implementing a secure and all-encompassing AI strategy that aligns with your organisation’s goals, values, and risk appetite. Our collaborative approach involves working closely with your team to develop a tailored strategy that addresses critical factors such as people, processes, technology, and culture. To meet your specific organisational needs, the strategy can include various key deliverables such as those mentioned in our market guide which you can access by getting in touch with us.

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To achieve responsible AI, it is crucial to have thorough planning, necessary tools, and effective governance in place to ensure unbiased, transparent, and explainable outcomes. SecAIS is committed to enhancing the credibility of your AI deployments by integrating responsible AI principles into your AI Security strategy. Our definition of responsible AI encompasses five key aspects:

AI systems must be capable of protecting customer privacy and safeguarding against potential attacks. To strengthen your overall security measures, implementing privacy enhancing technologies is a viable option that should be considered.

AI systems must prioritise inclusivity and accessibility, and must not lead to any form of unfair discrimination against individuals, communities, or groups.

AI systems must be transparent and allow for responsible disclosure to inform stakeholders when they are being impacted. This includes transparency in data, system and business models, as well as traceability mechanisms. AI systems should also be able to explain their decisions to stakeholders and disclose their capabilities and limitations.

AI systems must operate reliably, safely, and consistently, fulfilling their intended purpose under both normal and unexpected circumstances

Risk-based human involvement and oversight in the decision-making process.