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.
When AI produces audit work, what is the audit committee actually trusting?
Enterprises can satisfy security and governance requirements by combining traditional RBAC with modernized methods.
