In today’s fast-moving technology landscape, maintaining trust is essential.
Bias is a reliability problem. This creates not simply a fairness gap but a defensive gap.
In agent-driven environments, the matter of authorization becomes significantly more important.
AI-augmented prioritization helps route remediation efforts toward the riskiest vulnerabilities.
A checkbox mindset has become all too common in privacy. This has hindered innovation and created a trust barrier.
It is crucial for those who lead AI governance programs to effectively manage risk while enabling responsible AI innovation at scale.
A new audit model built on ongoing monitoring, automated data analysis, and continuous visibility is taking over.
Transparency and accountability are necessary for organizational governance during audits and post-incident reviews.
One organization struggled to demonstrate to auditors how alerts connected to the choices made on the engineering floor.
ISACA has established a solid foundation, but the incoming board chair wants to build on it.
Research on the availability of AI systems is lacking. This is a gap I would like to fill.
People looking for more value are going to choose additional features and better quality. Speed is a part of this equation.
There are five key considerations for developing effective agentic AI risk management and governance capabilities.
