Deploying agentic AI with safety and security: A playbook for technology leaders
- Lorenzo Ostili
- Oct 22
- 13 min read
The rise of autonomous AI agents brings transformative potential, but also a spectrum of emerging risks and complex vulnerabilities that must be addressed without delay.
Emerging risks in the agentic era |
Guiding principles for agentic AI security |
During the deployment of an agentic AI use case |
Agentic security cannot be an afterthought |
Autonomous AI agents are ushering in a new era of possibility—alongside a host of unprecedented risks and vulnerabilities that demand immediate, strategic attention.
Business leaders are rapidly embracing agentic AI, and the momentum is understandable.
These systems are autonomous, goal-driven, and capable of reasoning, planning, acting, and adapting without human intervention. Such capabilities hold the potential to transform how organizations operate, enabling them to fully harness the disruptive power of generative AI. From customer service and software development to supply chain optimization and regulatory compliance, agentic AI is projected to unlock between $2.6 trillion and $4.4 trillion in annual value across more than 60 use cases. Yet, this is only the beginning: just 1% of surveyed organizations consider their AI adoption to be mature.
However, with great potential comes equally significant risk. Agentic AI introduces a new class of vulnerabilities—ones that could compromise data integrity, disrupt operations, and erode stakeholder trust. These systems not only create fresh external attack surfaces but also pose internal threats due to their autonomous decision-making. In cybersecurity terms, AI agents function as “digital insiders”—entities with varying levels of system access and authority. Like human insiders, they can cause harm unintentionally through misalignment or, if compromised, act with malicious intent.
Alarmingly, 80% of organizations report encountering risky behaviors from AI agents, including unauthorized system access and improper data exposure.
This evolving landscape places a critical responsibility on technology leaders—CIOs, CROs, CISOs, and DPOs—to deeply understand the emerging risk profile of agentic AI and to lead secure, compliant deployments. Early adopters offer valuable lessons, from reengineering workflows to embedding observability and control mechanisms that mitigate risk at scale.
The future of AI in the workplace isn’t just about speed or intelligence—it’s about autonomy. Agents will increasingly initiate actions, collaborate across organizational boundaries, and influence outcomes in real time. This shift is exciting, but it also raises a fundamental imperative: in a world where agents act on behalf of the enterprise, trust cannot be an afterthought. It must be architected into the very foundation of AI strategy.
Emerging risks in the agentic era
As agentic AI systems take on tasks once reserved for human employees, they introduce a new dimension to the enterprise risk landscape. The paradigm shift is clear: we are moving from AI systems that facilitate interactions to autonomous agents that initiate and execute transactions—directly influencing core business processes and outcomes.
This evolution intensifies longstanding cybersecurity challenges, particularly around the foundational principles of confidentiality, integrity, and availability. In the agentic context, these principles are tested not only by the scale and speed of automation, but also by the potential amplification of systemic risks such as data privacy breaches, denial-of-service scenarios, and compromised system integrity.
Crucially, agentic AI introduces a set of emerging risk drivers that extend beyond the traditional AI risk taxonomy.
These include:
Autonomous decision-making without human oversight, which can lead to misaligned actions or unintended consequences.
rivilege escalation and lateral movement, as agents gain access to systems and data across silos.
Dynamic behavior adaptation, which complicates predictability and control.
Agent-to-agent interactions, creating complex interdependencies and potential feedback loops.
Opaque reasoning pathways, making it difficult to audit or explain agentic decisions.
These risks demand a reimagined approach to governance, observability, and control—one that aligns technological autonomy with organizational intent and trust.
And also:
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Chained vulnerabilities. A flaw in one agent cascades across tasks to other agents, amplifying the risks.
Example: Due to a logic error, a credit data processing agent misclassifies short-term debt as income, inflating the applicant’s financial profile. This incorrect output flows downstream to the credit scoring and loan approval agents, leading to an unjustified high score and risky loan approval.
Cross-agent task escalation. Malicious agents exploit trust mechanisms to gain unauthorized privileges.
Example: A compromised scheduling agent in a healthcare system requests patient records from a clinical-data agent, falsely escalating the task as coming from a licensed physician. The agent then releases sensitive health data, resulting in unauthorized access and potential data leakage without triggering security alerts.
Synthetic-identity risk. Adversaries forge or impersonate agent identities to bypass trust mechanisms.
Example: An attacker forges the digital identity of a claims processing agent and submits a synthetic request to access insurance claim histories. Trusting the spoofed agent’s credentials, the system grants access, exposing sensitive policyholder data without detecting impersonation.
Untraceable data leakage. Autonomous agents exchanging data without oversight obscure leaks and evade audits.
Example: An autonomous customer support agent shares transaction history with an external fraud detection agent to resolve a query but also includes unneeded personally identifiable information about the customer. Since the data exchange isn’t logged or audited, the leakage of sensitive banking data goes unnoticed.
Data corruption propagation. Low-quality data silently affects decisions across agents.
Example: In the pharmaceutical industry, a data labeling agent incorrectly tags a batch of clinical-trial results. This flawed data is then used by efficacy analysis and regulatory reporting agents, leading to distorted trial outcomes and potentially unsafe drug approval decisions.
Such errors threaten to erode faith in the business processes and decisions that agentic systems are designed to automate, undermining whatever efficiency gains they deliver. Fortunately, this is not inevitable. Agentic AI can deliver on its potential, but only if the principles of safety and security outlined below are woven into deployments from the outset.
For each agentic use case in an organization’s AI portfolio, tech leaders should identify and assess the corresponding organizational risks, and, if needed, update their risk assessment methodology.
Guiding principles for agentic AI security
To deploy agentic AI responsibly, organizations must embrace a structured, multi-layered approach that balances innovation with resilience. This roadmap offers a practical guide for technology leaders—CIOs, CISOs, CROs, and DPOs—to assess organizational readiness, mitigate emerging risks, and enable confident, compliant adoption of autonomous systems.
Prior to agentic deployment
Before an organization begins using autonomous agents, it should ensure that it has the necessary safeguards, risk management practices, and governance in place for a secure, responsible, and effective adoption of the technology. Here are some key questions to consider:
Does our AI policy framework address agentic systems and their unique risks?
Answering this question starts with upgrading existing AI policies, standards, and processes—such as identity and access management (IAM) and third-party risk management (TPRM)—to cover the new capabilities of agentic systems. For instance, in the context of IAM, organizations should define roles and approval processes for agents to protect interactions with data, systems, and human users. Similarly, they should define and review the interactions of agentic solutions acquired from third parties with internal resources.
As organizations accelerate their adoption of agentic AI, they must also contend with a rapidly evolving regulatory landscape. The first step is clarity: identifying which legal frameworks apply across jurisdictions and sectors.
In the European Union, Article 22 of the General Data Protection Regulation (GDPR) grants individuals the right to contest decisions made solely through automated processing—placing clear limits on certain AI applications. In the United States, sector-specific laws such as the Equal Credit Opportunity Act (ECOA) impose anti-discrimination requirements on AI systems used in credit and lending. At the state level, initiatives like New York City’s Local Law 144 mandate bias audits for automated employment decision tools, signaling a broader shift toward algorithmic accountability.
New AI-specific legislation is also on the horizon. The EU AI Act, for example, is being finalized and will take full effect within the next three years, introducing tiered risk classifications and compliance obligations for AI systems across industries.
Is our risk management program equipped to handle agentic AI risks?
Traditional enterprise cybersecurity frameworks—such as ISO 27001, the NIST Cybersecurity Framework (CSF), and SOC 2—are built around systems, processes, and people. While robust, they do not yet fully account for the emergence of autonomous AI agents that operate with discretion, adaptability, and decision-making authority.
To bridge this critical gap, organizations must revise their risk taxonomy to explicitly incorporate the novel threat vectors introduced by agentic AI. These include dynamic behavior, privilege escalation, opaque reasoning, and autonomous system-to-system interactions—none of which are adequately addressed in current frameworks.
For each agentic use case within the AI portfolio, technology leaders should:
Map agentic capabilities to organizational risk domains, including data governance, operational integrity, and access control.
Assess and quantify agent-specific risks, factoring in autonomy, adaptability, and potential for misalignment.
Update risk assessment methodologies to reflect the unique characteristics of agentic systems, ensuring that measurement tools can capture both direct and emergent threats.
Without this level of transparency and precision, agentic AI risks may evolve into operational blind spots—surpassing even the opacity challenges posed by analytical and generative AI. Proactive adaptation of cybersecurity governance is not optional; it is foundational to safe and scalable deployment.
Do we have robust governance for managing AI across its full life cycle?
Establishing effective governance for agentic AI requires a shift from traditional oversight models to standardized, proactive frameworks tailored to autonomous systems. This includes defining clear processes across the AI lifecycle—onboarding, deployment, and offboarding—with embedded accountability and control.
Key governance components include:
Ownership and Role Definition Assign explicit responsibilities for each phase of the AI lifecycle, ensuring that deployment decisions, operational oversight, and decommissioning protocols are owned by designated stakeholders.
Monitoring and KPI-Linked Anomaly Detection Implement observability mechanisms that track agent behavior against predefined performance and risk indicators. Define escalation triggers for anomalous activity or misalignment.
Accountability Standards for Agent Actions Establish clear rules of engagement for autonomous agents, including auditability, traceability, and thresholds for human intervention.
For each agentic AI solution in the portfolio, organizations should begin by cataloging:
Technical Attributes: foundational model, hosting environment, data sources accessed
Operational Context: use case criticality, data sensitivity, access privileges, inter-agent dependencies
Once mapped, governance should ensure:
Human-in-the-loop oversight for critical decisions
Named stakeholders for security, compliance, and operational integrity
Allocated capabilities to manage and mitigate agent-specific risks
This structured approach enables organizations to scale agentic AI confidently—aligning autonomy with enterprise intent, resilience, and trust.
Especially in the experimental or piloting stage, AI projects have a way of proliferating rapidly without adequate oversight, which can make it challenging to manage risks or enforce governance.
Prior to launching an agentic use case
Once the above foundational questions have been addressed and an agentic AI risk framework and policies are in place, organizations should develop a clear understanding of precisely what they are building, accounting for associated risks and compliance considerations for each project. Addressing the following questions can help ensure that their ambitions are matched by readiness:
How can we maintain control of agentic initiatives and ensure that we have oversight over all projects?
In the early stages of experimentation and piloting, AI initiatives often proliferate rapidly—frequently without adequate oversight. This decentralized momentum can obscure visibility, complicate risk management, and undermine governance efforts.
To mitigate these challenges, organizations should implement a centrally governed, business-aligned AI portfolio management system. This system must be designed to ensure consistent oversight by key control functions, including IT risk, information security, and IT compliance.
Key features of a robust AI portfolio management system include:
Transparent Ownership Mapping Clear delineation of business, IT, and security ownership for each AI use case.
Comprehensive Use Case Documentation Detailed descriptions of agentic applications, including purpose, scope, and operational context.
Data Inventory and Status Tracking A complete list of data inputs—used for training, interaction (e.g., connected APIs), or both—along with their sensitivity, provenance, and current status.
Lifecycle Visibility Inclusion of all agentic systems across stages: in development, actively piloted, or planned by business units.
By maintaining this centralized repository, organizations can prevent uncontrolled experimentation and reduce the risk of deploying models with unintended exposure points—whether technical, operational, or regulatory. This approach not only strengthens governance but also enables scalable, secure innovation across the enterprise.
Do we have the capabilities to support and secure our agentic AI systems?
TTo ensure the success of agentic AI pilots, organizations must begin with a clear-eyed assessment of their current capabilities—spanning skills, knowledge, and resources—against the demands of the agentic AI roadmap. This includes evaluating proficiency in:
AI security engineering
Security testing and validation
Threat modeling for AI models and agents
Governance, compliance, and risk management
Once baseline capabilities are mapped, organizations should identify gaps between agentic ambitions and current security maturity. These gaps often span both technical expertise and operational readiness.
To close them, leaders should:
Launch targeted awareness and education campaigns to build cross-functional understanding of agentic risks and responsibilities.
Define critical roles across the AI lifecycle, from model onboarding and deployment to monitoring and offboarding.
Upskill key personnel, such as security engineers, in specialized areas like threat modeling for autonomous agents and AI-specific attack surfaces.
For example, organizations lacking expertise in AI threat dynamics must prioritize training programs that equip security teams to anticipate, detect, and mitigate agent-driven vulnerabilities.
This proactive approach ensures that agentic AI pilots are not only innovative—but also secure, compliant, and aligned with enterprise resilience goals.
During the deployment of an agentic AI use case
Once use cases and pilots are up and running, organizations will need to ensure that the pilots are enforced by technical and procedural controls. These controls should be regularly reassessed to ensure that they remain relevant and effective as agentic systems are refined and scaled. Here are some key questions to consider :
Are we prepared for agent-to-agent interactions, and are those connections secure?
As agentic AI systems evolve, their interactions are no longer limited to human users—they increasingly engage with other AI agents. This shift toward multiagent ecosystems introduces a new layer of complexity and risk, making it essential for organizations to secure agent-to-agent communications with the same rigor applied to human-machine interfaces.
Emerging protocols—such as Anthropic’s Model Context Protocol, Cisco’s Agent Connect Protocol, Google’s Agent2Agent, and IBM’s Agent Communication Protocol—are being developed to manage these interactions. However, most remain in early stages and lack the maturity required for enterprise-grade deployment.
In the meantime, technology leaders should take proactive steps to safeguard interagent collaboration:
Authenticate all agent communications to prevent spoofing or unauthorized access.
Log interactions for auditability, traceability, and forensic analysis.
Enforce permission boundaries to ensure agents operate within defined scopes and access levels.
Waiting for perfect standards is not a viable strategy. Organizations should implement interim safeguards now, while maintaining flexibility to upgrade as protocols mature and regulatory expectations evolve.
Securing agentic collaboration today lays the groundwork for resilient, scalable AI ecosystems tomorrow—where trust is engineered, not assumed.
Do we have control over who can use agentic systems and whether they are using them appropriately?
Access to models and resources must be both monitored and securely controlled. Identity and Access Management (IAM) systems should extend beyond human users to include AI agents that interact with other agents, people, data, and digital infrastructure. Organizations need to establish clear policies defining who—whether human or AI—is authorized to access or interface with specific resources, as well as the conditions and contexts under which that access is permitted.
To strengthen protection, IAM frameworks should be enhanced with input and output guardrails that prevent AI agents from being manipulated, misused, or driven toward unsafe behaviors, such as responding to adversarial prompts or pursuing misaligned objectives.
Moreover, organizations must carefully oversee interactions involving third-party AI agents, ensuring that any external systems connecting to internal environments comply with the same standards of security, governance, and ethical integrity applied to in-house technologies. This holistic approach reduces exposure to risks while maintaining accountability and trust across the AI ecosystem.
Can we trace agents’ actions and understand and account for their behavior?
To ensure responsible deployment, agentic AI systems must be designed with traceability mechanisms from the outset. This goes far beyond logging surface-level outputs—it requires capturing the full behavioral lineage of each agent, including:
Prompts and inputs that initiated agent actions
Decisions and intermediate reasoning steps
Internal state changes throughout execution
Final outputs and system-level impacts
This level of traceability is essential for:
Auditability: enabling transparent reviews of agent behavior
Root cause analysis: diagnosing failures or unintended outcomes
Regulatory compliance: meeting evolving standards for explainability and accountability
Post-incident reviews: informing remediation and future safeguards
To maintain alignment over time, organizations should also implement regular performance reviews of agentic systems. These reviews should assess whether agents continue to operate in accordance with their intended purpose, ethical boundaries, and enterprise risk posture.
Traceability isn’t just a technical feature—it’s a strategic imperative for building trust, resilience, and long-term viability in agentic AI ecosystems.
Do we have a contingency plan if an agent fails or behaves unexpectedly?
Even the most carefully engineered AI agents can malfunction, become compromised, or be exploited. Therefore, before deployment, organizations should establish a comprehensive contingency plan for every critical agent, incorporating rigorous security and recovery protocols. The process begins with simulating worst-case scenarios—for instance, agents that fail to respond, deviate from intended goals, act maliciously, or escalate tasks without authorization.
Once vulnerabilities are understood, organizations must ensure that termination mechanisms and fallback systems are in place, allowing for immediate shutdown or redirection of operations if an agent behaves unpredictably. These measures help contain risks and minimize disruption to business processes.
Finally, agents should be deployed within self-contained, well-defined environments, where network and data access are tightly controlled. This setup enables rapid isolation and intervention when necessary, protecting the broader system from cascading failures or security breaches. By combining foresight, testing, and containment, organizations can significantly strengthen resilience and maintain operational integrity in the face of unexpected AI behavior.
By identifying and implementing effective controls, organizations can proactively mitigate agentic AI risks rather than reactively responding to them. For instance, maintaining a consistent AI agent portfolio alongside robust AI logging enables the monitoring of data exchanges between agents, thereby mitigating the risk of untraceable data leakage. Additionally, deploying an AI contingency plan and sandbox environment, in conjunction with IAM and guardrails, can effectively isolate an AI agent that attempts unauthorized privilege escalation through cross-agent task escalation.
Agentic security cannot be an afterthought
The rise of the agentic workforce is no longer speculative—it is inevitable. As organizations increasingly deploy autonomous AI agents, new and complex challenges will emerge around safeguarding the confidentiality, integrity, and availability of data and systems.
Technology leaders now face a pivotal inflection point: how to balance the transformative potential of agentic AI with a disciplined, forward-looking approach to risk management. The stakes are high—no enterprise wants to become the cautionary tale of the first agentic AI security breach.
To avoid this, CIOs, CROs, CISOs, and other senior stakeholders must initiate cross-functional dialogue to gain full transparency into the current state of agentic AI adoption across the organization. These conversations should lead to the development of foundational guardrails—governance structures, security protocols, and accountability frameworks—that enable safe scaling without compromising trust.
While today’s agentic transactions remain digital, the trajectory is clear: the future will include embodied agents operating in physical environments, from robotics to autonomous vehicles and beyond. This evolution will dramatically expand the safety and security perimeter, making it even more critical to lay a resilient foundation now.
Acting with intention, structure, and foresight today is not just prudent—it’s essential for ensuring that agentic AI becomes a source of competitive advantage rather than operational risk.
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Article written by: Lorenzo Ostili
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