Redefining risk: The agentic AI revolution in enterprise risk management

Modern risk management is evolving to meet today's challenges. As regulations tighten, cyberthreats grow, and operations become more complex, businesses have the chance to adopt smarter, more adaptive solutions that will help them stay ahead of risks and drive confidence and resilience.

This is where agentic AI comes in. With capabilities like autonomous decision-making and problem-solving, agentic AI enables organizations to adapt, respond, and thrive in volatile business landscapes.

This blog explores how agentic AI is transforming key processes across risk management. We'll also highlight practical applications and share a roadmap for integrating agentic AI into your risk protocols. 

For decades, risk teams have leaned on checklists, static audits, and manual reviews to assess compliance and enforce controls. While these traditional methods have served enterprises well, they're not up to the task of staying ahead of new and emerging risks in a complex world. Here's why:

• Manual processes can be slow and inaccurate: They take up precious time and effort that would be better spent on strategic work

• Static audits miss critical anomalies: Limited data sampling often overlooks emerging vulnerabilities or shifting compliance requirements

• Evolving risks outpace detection: Historical data and reactive mechanisms can fail to recognize new types of cyberattacks or third-party risks

   

The challenge is significant, but so is the opportunity. With AI, enterprises can reimagine risk management, shifting from hindsight to foresight to empower decision-makers.

AI isn't just a tool to improve current practices; it's a fundamental shift in how risks are identified, assessed, and mitigated. Where once risk practitioners relied on historical data sampling, AI uses cutting-edge technologies such as machine learning (ML), natural language processing (NLP), and predictive analytics to help them expand coverage and identify patterns and exposures before they escalate into crises.

AI transforms risk management in five key ways:

Agentic AI represents the next frontier in risk management. While traditional AI relies on preset algorithms and static models, agentic AI acts independently, continuously learning and adapting to execute complex workflows and deliver outcomes with minimal human involvement.

What does this mean for risk management? Agentic AI enables a more autonomous, strategic function that stays one step ahead of a rapidly evolving risk landscape. AI agents can help organizations to constantly monitor changing risk profiles, spot vulnerabilities with precision, and act in real time. This allows organizations to proactively address emerging risks before they become major disruptions.

Agentic-AI-led transformation of enterprise risk isn't just possible, it's happening right now. Here's a look at real-world applications across enterprise risk and compliance:

1. Real-time internal controls assurance

The traditional life cycle for internal controls – from scoping and testing to reporting – involves lengthy cycles of audits, gap analyses, and remediation efforts. The result? Operating loopholes and controls that can't keep up with new fraud techniques. With agentic AI, fraud indicators and outliers are flagged and anomalies are analyzed in real time.

We envision that agentic AI can accelerate and enhance the control assurance process with three interconnected agents:

• Planner AI agent: Analyzes financial statements and past exception reports to recommend material processes and entities for review

• Designer AI agent: Uses NLP to read SOPs and process maps to create control matrices, detailed gap reports, root cause analysis, and LLM-powered remediation

• Tester AI agent: Automates evidence gathering, intelligent sampling, and results validation

   

These three AI agents work in tandem with each other, supported by tailored digital frameworks and governance, risk, and compliance platforms, to enable efficient, continuous monitoring.

2. Reimagining internal audits

Static, sporadic audits are giving way to agile, AI-driven processes. AI-driven audits can be near-real-time, providing ongoing assurance rather than just point-in-time reports.

Additionally, by acting as a virtual auditor, agentic AI can help review the entire population, ingesting data from multiple sources – both structured and unstructured – and using NLP to interpret policy, contracts, and other documents.

Agents to deploy include:

• Dynamic risk assessment agent: Continuously reviews structured and unstructured data (both historical and external) to spot potential risks and guide internal audit plans

• Audit program designer agent: Examines past audit reports, industry standards, and regulatory requirements to define the scope, objectives, and key focus areas for audit programs

• Document review agent: Analyzes text-based data like contracts, emails, and reports to identify potential legal, regulatory, or reputational risks, working with other agents to create audit plans, scope, and fieldwork

• Intelligent auditor agent: Performs data analytics and sample testing based on defined controls

• Report writing, dashboard agent: Generates standard and on-demand reports by geography, business unit, and end-user with tailored conversational insights

3. Scalable third-party risk management (TPRM)

Distributed data and manual vendor evaluations often bring blind spots into third-party risk frameworks. Agentic AI addresses these gaps by monitoring risk continuously with AI-powered risk exchanges – risk intelligence marketplaces where organizations, regulators, and vendors contribute and access live third-party risk data from multiple sources.

AI-powered systems that simplify third-party evaluations include:

• Automated inherent risk grading agent: Analyzes historical data and benchmarks to identify risks. Machine learning predicts risk likelihood and grades them by service type and location

• Due diligence and document review agent: Uses NLP to assess third-party questionnaire responses, evaluating compliance against standards like ISO 27000 and ISO 31000 and the organization's code of conduct

• Contract review agent: Reviews vendor contracts during onboarding to check they are comprehensive and include all necessary terms, service-level agreements (SLAs), and obligations

• Report writing agent: Creates a comprehensive third-party screening report and offers mitigation recommendations

• Screening and continuous monitoring agent: Identifies risk proactively for existing and emerging risk drivers like sanctions, litigations, and human rights to sound early warning alarms for geopolitical risks and financial instability

4. Driving value with post-payment audit

Post-payment audits (PPAs) are evolving from manual, reactive processes into AI-powered systems of insight and orchestration. Advanced AI tools are helping turn complex data into actionable insights, automating claim creation and streamlining workflows. Additionally, organizations can use AI agents to autonomously standardize data, scan for anomalies, and pinpoint support documents for claim reviews.

The results are tangible. Companies can speed up recovery processes, cut audit time, and reduce financial risks.

Agents in the PPA process can include:

• Data acquisition and transformation agent: Cleans, standardizes, and integrates data from various sources, using advanced NLP to draw valuable insights from unstructured text

• Audit agent: Uses NLP to extract keywords, clauses, obligations, and risks from emails and contracts, organizing them by type, risk level, and compliance. It also detects anomalies to flag potential fraud or errors

• Claim pack collaboration agent: Automatically generates and categorizes claims based on audit findings and delivers AI-generated insights into audit performance, cash recovery rates, and supplier compliance

The impact is real. With our AI-powered Post-Payment Audit solution, a Fortune 100 retailer's savings nearly doubled, from $15 million to $28 million in one year, hitting $100 million over three years.

Additionally, a food distribution company used the solution to reduce financial leakages by about 70%, harnessing the power of generative AI, advanced analytics, and our audit expertise to gain $1 million in profit and loss (P&L) savings.

5. Fortifying cybersecurity and access management

Cybersecurity and IT risk management are critical to building trust in today's interconnected environment. Traditional cybersecurity frameworks rely on periodic testing and are prone to human error. Agentic AI can help by creating self-adjusting defense mechanisms that accurately identify risks in enterprise IT environments.

• Cybersecurity maturity assessment agent: Evaluates cybersecurity practices by measuring them against industry standards to identify areas of improvement

• Gap assessment and controls mapping agent: Compares an organization's security practices to standards like NIST or ISO, enabling adequate coverage

• Mitigating controls mapping agent: Aligns mitigating controls with unresolved segregation of duty (SoD) risks, promoting comprehensive coverage and stronger safeguards

• Role redesign agent: Revamps roles intelligently to reduce SoD risk, automatically generating role descriptions based on role content and assigning them to users based on job responsibilities

To truly revolutionize risk management, organizations must think beyond incremental improvements. Agentic AI provides a framework for a proactive, strategic approach that delivers resilience, scale, and precision.

Risk doesn't have to hold your organization back. With the right tools, it can propel your business forward. The future of risk management isn't in containment; it's about empowerment. And the potential is as limitless as the technology driving it.