At AIShield, we have had an impressive year of growth and achievements. Our customers are delighted and so does our partners. All thanks to the A-team that has consistently demonstrated adaptability and a focus on pivoting when necessary, allowing us to achieve significant growth in our product, business, and team. To drive this growth, we have implemented several strategic initiatives, including an API-first product, targeting key industries, offering free product trials, hosting and launching our product on AWS, building demos, releasing white paper, enabling free security assessment, deploying defenses across the multi-cloud to edge continuum, and providing reference implementations with a python SDK. These efforts have helped us attract and serve many customers and have laid a strong foundation for our business moving forward.
Our focus on AI security has enabled us to develop innovative technology that sets us apart from the competition. As a result, we have gained a significant advantage over our competitors. We have established successful partnerships thanks to our technology leadership. In the constantly changing landscape of AI and market dynamics, we must adapt and offer new solutions as customer preferences shift due to the move from piloting to full-scale production of AI to achieve favorable business outcomes.
Our customers are using AIShield to meet AI Security needs. However they are finding it particularly difficult to test and evaluate their AI systems on trustworthy, and responsible AI aspects, such as performance assurances, explicability, fairness and bias, drifts, and monitoring across the model lifecycle (MLOps). In many cases, we see that the validation and verification processes are confused and produce undesirable outcomes for the organization. According to our analysis of 81 MLOps players based on a report published by the AIIA, only 27% indicated having full capabilities in model testing and validation area, while 13% have partial capabilities. Notably, only 4% of companies offer model validation and machine learning security, and none have significant AI security experience and focus. Additionally, piecemeal, fragmented, and intrusive open-source solutions are available for both model validation and security, but organizations have a hard time implementing them. Organizations sometimes face challenges in validating their models due to technical, regulatory, contractual, organizational, and policy reasons, as they may not have access to the necessary data. Overall, the gaps in the testing and evaluation of AI have a significant impact on the strategic, financial, and operational aspects of AI-first businesses.
We use strait metaphors to understand the T&E challenges of an organization. A strait is a narrow body of water that connects two larger bodies and often separates two land masses. Straits play a significant role in the movement of people, goods, and information between regions. They can have substantial economic and strategic value as they serve as important channels for the flow of value between different parts of the world. In business, we can compare gaps as straits that disrupt the business value flows. At AI-first businesses, the challenges of T&E are akin to the strait and can disrupt the business. The straits are gaps at strategic, financial, and operational levels and are deeply rooted in AI risks at testing an evaluation phase in AI-powered businesses. We realized in the past year, organizations — large and small, new and old, leaders and laggards are choking at those straits. The resulting impact is slower adoption of AI at scale and lost opportunity to positively impact millions of people today and in the short future. This is the antithesis of AI and also the antithesis of AIShield.
It is not in our nature to do nothing when we see struggling customers and an opportunity to deliver value. The antithesis is unbearable for us. We had an itching desire to do something and felt that we could do something with the help of our technology, product, knowledge, and team. And now, we have found a solution or at least a way to address these issues.
>aishield.STRAITE is a comprehensive approach, concept, solution, and product family we have envisioned. STRAITE is also an acronym for secure, trustworthy, responsible AI testing and evaluation. Use AIShield to deliver STRAITE.
The cornerstone of it is the combination of low-data and no-data approaches and a security-first approach to facilitate testing and evaluation of AI.
But before we dive deep into </>aishield.STRAITE— let’s understand key components of STRAITE. We see that AI Testing and evaluation is like a three-layer onion where the core part is security, the next layer is trustworthy, and the top layer is responsible AI.
Layers in AI Testing & Evaluation
- AI Testing & evaluation: Process of assessing the performance and functionality of artificial intelligence (AI) systems. It includes functional testing, verifying that the AI system can perform its intended tasks accurately and consistently, and non-functional testing, which focuses on evaluating the system’s performance, reliability, and other characteristics.
- Responsible AI: Ethical and responsible design, development, and deployment of AI systems that consider potential impacts and consequences on individuals, societies, and the environment. Ensures accountability, transparency, and explainability in decision-making processes and promotes inclusivity and fairness in development and use. Responsible AI is important because it involves considering AI’s potential impacts and consequences on individuals, societies, and the environment and taking steps to mitigate any negative impacts and maximize the positive ones.
- Trustworthy AI: Reliable, accurate, transparent, fair, and aligned with societal values and ethical principles. It can be trusted by humans to make safe and values-aligned decisions and actions.
- Secure AI: Protected against malicious use, modification, or destruction. Able to function correctly in the presence of malicious actors or attacks. Designed and deployed with security measures to identify, protect, detect and defend against cyber threats and vulnerabilities and ensure confidentiality, integrity, and availability of AI systems. Protects against vulnerabilities and misuse that can have severe consequences for individuals, organizations, and society.
</>aishield.STRAITE aims to connect the various stakeholders involved in AI — producers and consumers, development and deployment, developers and defenders — on a level of trust, responsibility, and security, respectively. By doing so, we hope to enable organizations to move from a large pool of investment to a large pool of return, all while improving their security posture and trust.
</>aishield.STRAITE aims to deliver STRAITE with its solutions, product family, and partnership engagements. We are addressing parts of STRAITE already today. Product delivering security component. Specific solutions powered by product and homegrown capabilities augmented with open source components delivering partial STRAITE. Numerous partnerships are delivering adjacencies to STRAITE. However, they are not delivering on all aspects of STRAITE with the needed maturity for organizations. Therefore, we will have to evolve and do things differently going forward while staying true to our original mission of securing the AI systems of the world. Here is our three-prong approach.
- Product Family: We plan to create a comprehensive product family with a security-first approach to enable STRAITE. This will involve strengthening our existing products AIShield and developing new ones based on our learnings from solutions. The product family aims to provide the organization with both the base capabilities of STRAITE and advanced security capabilities in a scalable and sustainable way. Our product-led growth paradigm will support the product family. We will offer free trials, reference implementations, SDKs, and support for open-source projects.
- Solution: We will continue researching and developing solutions based on market needs. Solutions will lead, and products will follow. Our solutions will also prioritize performance, explicability, fairness, bias, and model drift. To measure performance, we will provide accuracy metrics, the average runtime of the model/query, and robustness metrics. For explicability, we will offer global explanations using surrogate model approaches and contrastive analysis (What-if scenarios). To address fairness and bias, we will use proxy approaches to provide top-level assurance and generate corner cases to validate it further. To detect model drift, we will build a model drift detector. These measures will ensure that STRAITE is fulfilled at a foundational level and security at an advanced level, without the need to provide access to or share data.
- Partnerships: We will also continue to develop partnerships to further mature STRAITE. We are deepening our collaboration with existing partners with renewed focus. We are continuously searching for new ones who can bring advanced capabilities in area of trustworthy and responsible AI and augment AIShield advanced capabilities in securing AI.
Our evolutionary adaptation from AIShield to </>aishield.STRAITE represents our commitment to helping build secure, trustworthy, and responsible AI solutions that both producers and consumers of AI can trust. We are confident that this evolution will enable us to do our bit in the world by accelerating AI adoption at scale, minimizing risks, and improving AI-first organizations’ security posture and trust. With </>aishield.STRAITE, we aim to empower developers and allow businesses to tap into the full potential of AI.
We believe that trustworthy and responsible AI begins with secure AI.
Do not fear the straits
Embrace them with </>aishield.STRAITE
Conquer them straight with AIShield
Have a smile, This is the Way