Ramp up product security teams for an AI first-world.
Continuous assessment of AI systems from a cybersecurity perspective is crucial to ensure that any organizational AI implementations are robust, secure, and resilient against evolving cyber threats.
In a rapidly evolving digital landscape, the integration of Artificial Intelligence (AI) is becoming increasingly mainstream. As technology-focused businesses harness the power of AI to enhance their products and services, the role of Cybersecurity (mainly product security teams) has never been more critical. These teams must continually ramp up their skills and adapt to the dynamic AI-first environment.
This blog post is the first of a three-post series to provide a template for ramping up an organization's Product Security teams for an AI-first world of software development. The series will explore why Product Security teams need to stay ahead of the curve and discuss the pivotal role they play in securing products built upon AI-driven technologies.
We start with …
Cyber alignment with Organizational AI strategy
To be successful with an Organizational AI strategy, it is critical that the strategy not only supports organizational values and acceleration of technological innovation but also considers security and privacy implications at all stages. For AI systems, any retrospective rollout of security/privacy requirements will be more challenging vis-a-vis systems without AI capabilities.
It is a critical first step to identify AI focus areas of the organization over the next 24-36-60 months. If there is no Organization-level AI strategy, then whatever we discuss in this blog probably won't apply to you.
For cybersecurity (product security in particular) teams within technology companies to be an enabler for an organization's AI strategy, it is essential to proactively look at the organizational goals with a security lens to identify capabilities required to support the journey towards those goals. Do a gap assessment on the current capabilities of the product security teams versus the skills that will be needed as the organization starts executing its AI strategies.
As AI starts to build better AI, teams responsible for product security assessments and validations must build the frameworks that work for the organization. Leverage general security standards that are in active development. However, as is with any such available standards, it will require a custom brush to work within unique organizational constraints.
Without a security team's solid grasp on underlying technologies that will drive the AI-first wave of software development, it will not be trivial to build secure products —asking "*GPT how to secure a system?" will only go so far!
Alongside efforts to find alignment with the organizational AI strategy, define clear objectives for AI integration into cybersecurity practices. Identify areas where AI can be applied, such as security architecture, application security, threat detection, incident response, or data analysis. These objectives will guide the Cybersecurity team's training and development efforts.
Gap analysis of your current product security capabilities against skills your team will need as the AI strategy is executed.
Create a clear roadmap for training and development of the product security teams based on the identified gaps.
Cybersecurity Assessments Delivery Framework for AI-first world