Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Here's something that should keep you up at night: cybercrime might cost the global economy $10.5 trillion every year by 2025. That's not a typo. Traditional security measures? They're already struggling to keep pace. Attackers have figured out how to weaponize artificial intelligence, launching sophisticated campaigns that waltz right past conventional defenses like they're invisible.

In this new wave of machine-driven decision-making, the paradigm shift in artificial intelligence towards increasing autonomy is becoming increasingly significant. Autonomous or agentic AI systems, those capable of acting on their own and acclimatising themselves to new environments, are redefining the space by taking actions towards a goal without direct human intervention. Although this is exciting in terms of what it will enable for AI driven processes and creativity, it also introduces a more advanced set of security risks to contend with when dealing with autonomous based AI systems.

Implementing effective data governance in an organization requires a strategic approach that encompasses several key components. The first step is to establish a clear vision and objectives for data governance. This involves defining what data governance means for your organization and identifying the specific goals you aim to achieve. These goals could include improving data quality, ensuring data security, or enhancing data accessibility.