Artificial intelligence is becoming part of core business operations, from productivity platforms to autonomous agents that connect with data, tools and enterprise systems. As adoption expands, organizations need security controls that address how AI systems are identified, governed, monitored, tested and investigated when something goes wrong.
30%
of respondents identify security and privacy concerns as the second greatest inhibitor to AI deployment.
AI security requires more than a traditional application security review. Your organization needs a coordinated approach across identity, financial operations, forensic readiness and offensive security testing. RSM helps you assess AI risks, strengthen security controls and prepare for incidents involving AI-enabled systems, agents, large language models and connected enterprise platforms.
Across AI environments, the practical questions for leadership are clear: Which AI systems exist, who owns them, what can they access, how are they monitored and how quickly can risky activity be contained? These questions are especially important for agentic AI, where a single request can move through an orchestrator, multiple agents, third-party tools, APIs and systems of record.
Questions AI security should help leadership answer
Business leaders evaluating AI security should be able to answer the following questions:
- Which AI systems, agents and AI-enabled workflows are in use across the organization?
- Who owns each AI system, agent or workflow?
- What data, APIs, tools and business processes can each AI system access?
- Where can AI systems act without human review?
- How are AI activity, delegated access and agent actions logged?
- What controls limit access if an agent behaves unexpectedly?
- How would the organization investigate and contain an AI-related incident?
RSM’s solutions are deeply rooted in the five laws of AI security:
Because AI risk isn't just about attacks, it shows up to AI, through AI and from AI.
AI security services designed for emerging enterprise risk
Our AI security services focus on four areas that help organizations manage the risks introduced by AI systems, autonomous agents, large language models and supporting technology environments.
AI agents introduce a new class of identity and access risk: they act autonomously, invoke tools, delegate to subagents and directly access enterprise systems. RSM helps your organization put identity-first controls around every agent through a unified control plane—applying the same rigor and standards to human and nonhuman identities alike. This effort spans the full agent lifecycle: from discovery and first-class authenticated identity, through least-privilege delegated authority and just-in-time provisioning, to ongoing access review, runtime guardrails and clean termination with immutable audit. The result is a governed agent estate where every agent is known, owned, scoped to least privilege and monitored in real time—under one consistent model of identity governance.
AI FinOps builds upon the financial management disciplines established for cloud environments, extending them to the unique demands of AI models, agents, data consumption and inference workloads. It serves as a bridge between AI governance and financial accountability, helping your organization align AI investments with business outcomes while maintaining transparency and oversight.
When combined with broader AI governance and cybersecurity programs, AI FinOps enables your organization to manage AI risk, control costs, monitor performance and establish the governance framework needed to scale AI responsibly and sustainably.
AI incidents can involve stolen API keys or credentials, prompt injection, model misuse, data leakage, poisoned data sources, compromised AI pipelines, unauthorized agent activity and AI-enabled fraud. RSM helps your organization prepare for and respond to these events through response planning, logging and evidence readiness, tabletop exercises, forensic investigation, exposure analysis, containment support and executive response planning.
AI penetration testing evaluates AI, agentic AI and large language model environments to identify exploitable weaknesses, assess trust boundaries and test the effectiveness of controls and guardrails. Testing may include prompt manipulation, data exposure paths, unsafe agentic actions, integrations, plugins, APIs and connected data sources.
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