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Gartner has predicted that by 2028, 50% of enterprises will rely on AI security platforms to secure third-party and custom AI applications. These platforms will play a critical role in centralizing visibility, enforcing policies, monitoring activities, and applying consistent guardrails to manage security risks effectively.

Source: Gartner

What to know:

• AI security platforms are forecasted to become integral to enterprise incident response strategies by 2028.
• These platforms will provide real-time responses to potential security threats and track the behavior of AI systems.
• The shift reflects increasing recognition that AI systems, including GenAI tools, must be actively monitored to mitigate risks.
• Such platforms will enable businesses to create secure GenAI adoption strategies, ensuring compliance with organizational governance policies.

Why it matters:
For mid-sized businesses integrating GenAI, Gartner's prediction highlights the growing need for AI security solutions that centralize control and actively monitor usage. As GenAI adoption grows, the security risks associated with third-party and custom AI applications become more significant. AI security platforms will be critical in enabling businesses to stay ahead of threats, protect sensitive data, and maintain compliance.

As businesses increasingly embed AI into daily workflows,  from customer service and development to analytics and operations, new security risks emerge, expanding the overall threat surface. A recent report highlights that AI adoption is outpacing organizations’ ability to govern and secure these integrations, making proactive governance and risk mitigation essential.
Source: digwatch 

What to know:

• Integrating AI directly into workflows exposes systems to new attack vectors and misconfigurations that traditional security models are not designed to detect or mitigate.
• As AI tools automate tasks and interact with data, the attack surface expands,  including risks such as shadow AI, prompt injection, and over‑privileged access.
• The pace of adoption often outstrips the development of formal governance and monitoring controls, leaving gaps in visibility and oversight for enterprise risk teams.
• Analysts note that executive confidence often overestimates security readiness; many organizations lack real visibility into how AI workflows access data, tools, and external systems.
• Without integrated governance and continuous monitoring, workflow‑embedded AI tools can inadvertently expose sensitive data or trigger unauthorized actions.

Why it matters:
Embedding AI into business workflows accelerates productivity but also creates new classes of risk that traditional security postures aren’t built to address. As organizations transition AI from experimentation to production use, the complexity of interactions among AI, applications, and data flows requires robust governance frameworks, real‑time monitoring, and integrated security controls to mitigate threats arising from expanded attack surfaces such as autonomous actions, API connections, and unmonitored workflows.