As 2025 draws to a close and some organizations slip into a quieter holiday rhythm, their AI systems continue humming in the background—summarizing customer inquiries, triaging security alerts, generating code, and synchronizing records across critical systems. Within that uninterrupted activity, however, lies a less festive truth: agentic AI introduces cyber risks of unprecedented complexity and novelty, beyond what conventional architectures were designed to manage.
Agentic AI—the class of systems that can reason, plan, act, and adapt toward goals with reduced human oversight—promises measurable gains across legal services, finance, healthcare, and supply chain operations. But the same autonomy that drives new efficiencies also creates a distinctly complex cybersecurity risk profile. By initiating actions, calling tools, exchanging data with other agents, and escalating privileges to meet objectives, autonomous systems expand the attack surface and introduce “digital insiders” that can err at scale, leak data silently, and even be co-opted by threat actors. For those advising on governance, cyber preparedness, and emerging-tech strategy, the takeaway is clear: companies need a practical, defensible program tailored to agentic environments—one that reduces the likelihood and blast radius of failures before a single misaligned step turns out all the lights.
How Agentic AI Altered the Cyber Risk Landscape
In 2025, agentic AI quietly shifted from pilot to production as enterprises began embedding autonomous capabilities by default, business units launched agents to streamline their workflows, and vendors recast core products as “agent-enabled.” What began as contained experiments quickly became always-on operational systems, sometimes without formal governance, lifecycle management, or security controls.
Agentic systems intensify familiar cyber principles—confidentiality, integrity, availability—because agents do not just predict or summarize; they transact across systems and data. In practice, agentic deployments reshape risk along five fault lines.
- First, agent orchestration increases dependency risks. With multi-step plans and multi-agent workflows, a single defect can propagate. A logic error or corrupted data in an upstream agent can cascade into downstream approvals or transfers, amplifying harm and complicating root-cause analysis.
- Second, agent-to-agent trust creates new paths for privilege escalation. Compromised or malicious agents can spoof identities, “borrow” trust from peers, and request data or actions beyond their least-privilege allocation. Synthetic identity risks arise when adversaries forge agent credentials or exploit immature inter-agent communication protocols.
- Third, autonomy increases untraceable data movement. Agents routinely retrieve, transform, and share context among tools and peers. Without deliberate logging of prompts, for example, organizations may face “invisible” leakage of personal data, trade secrets, or privileged material.
- Fourth, external attack surfaces broaden. Prompt-injection, jailbreaks, and tool hijacking can redirect an agent’s objectives toward data exfiltration or fraud. Emerging attacker tactics, techniques, and procedures (“TTPs”) exploit AI to automate reconnaissance and craft highly targeted social engineering and malware. Meanwhile, immature vendor practices and open-source components introduce third-party and supply-chain exposure. In November 2025, Anthropic reported disrupting a largely agent-orchestrated espionage campaign that reportedly jailbroke Claude, decomposed malicious goals into benign-seeming subtasks, and leveraged tool integrations to automate recon, credential theft, and lateral movement at machine speed.
- Finally, governance gaps are common. Enterprise frameworks like ISO 27001, SOC 2, and NIST CSF were not designed for autonomous actors with discretion. Without updated taxonomies and oversight processes, agentic risk becomes a new “black box,” complicating legal risk assessment, regulatory engagement, and board oversight.
Key Regulatory Developments
In the United States, many regulators are intensifying scrutiny of artificial intelligence, leveraging existing privacy, security, and disclosure frameworks while new state AI laws are being enacted. Agencies have pursued enforcement actions based on “AI washing” and misrepresentation, and sector-specific regimes—including comprehensive state privacy laws, HIPAA, consumer protection statutes, financial services regulations, employment standards, and securities disclosure rules—continue to apply. In the absence of comprehensive federal regulation, states have taken the initiative to pass laws requiring AI companies to test their models for safety, strengthen consumer privacy protections, and ban deepfakes that could impact elections. According to the National Conference of State Legislatures, all 50 states have introduced AI-related legislation, including California’s transparency requirements for generative AI systems and Colorado’s obligations for high-risk AI deployers.
Against this backdrop of legislative activity, further changes are already on the horizon. On December 11, 2025, President Trump issued an executive order that seeks to curb laws limiting artificial intelligence and block states from regulating the rapidly evolving technology, sparking immediate debates over federal preemption and setting the stage for legal challenges. Nonetheless, cybersecurity remains a focal point for courts and regulators, who have expressed concerns about data leakage and confidentiality risks associated with public AI tools. Between 2023 and 2025, notable incidents and regulatory responses have included platform vulnerabilities that exposed user data, court-issued orders mandating heightened caution with sensitive litigation materials, and official guidance promoting secure software development and the use of AI-enhanced cybersecurity measures. Taken together, these developments have raised the bar for organizational diligence, underscoring the imperative for robust vendor management, disciplined data stewardship, and resilient internal controls.
Meanwhile, in the European Union, the AI Act introduces a risk-based regulatory framework with specific cybersecurity requirements for high-risk systems. These include mandates for accuracy, robustness, cybersecurity, automatic logging for traceability, comprehensive technical documentation, and human oversight. In financial services, the EU’s Digital Operational Resilience Act (“DORA”), effective since January 17, 2025, harmonizes ICT risk management, major incident reporting, resilience testing—including threat-led penetration testing—and oversight of critical third-party providers. These requirements directly impact agentic AI toolchains and vendor ecosystems, underscoring the need for robust operational resilience and compliance strategies.
Building Resilience: Strategic Steps for Agentic AI in 2026
Organizations can embrace agentic AI without inviting unmanaged risk. The path forward is pragmatic: upgrade governance and identity foundations, engineer containment and observability by default, and backstop innovation with legal, operational, and reputational safeguards capable of withstanding scrutiny.
- Strengthen policy and risk taxonomy for agents. Begin by updating AI policies, standards, and risk registers to address the unique risks posed by autonomous agents. This means extending identity and access management to non-human identities, with clear role definitions, approval workflows, and credential lifecycle controls. Integrate third-party risk management to scrutinize agent vendors’ training data sources, model behaviors, and security postures.
- Establish portfolio governance and oversight. Create a comprehensive AI register that inventories every agentic use case, detailing model specifics, hosting environments, data sources, sensitivity levels, privileges, dependencies, and business criticality. Implement a gated approval process for development, pilots, and production, ensuring defined ownership, human accountability, and escalation triggers. This centralized oversight prevents “shadow AI” and enables consistent audits.
- Engineer for least privilege, segmentation, and containment. Treat agents as privileged service accounts and enforce granular, context-aware access controls. Segment agent runtimes and networks, isolating high-risk agents in sandboxes with strict egress controls. Define clear termination and “kill switch” mechanisms, and practice incident isolation for scenarios involving cross-agent escalation.
- Implement security, monitoring, and vendor diligence. Securing agentic AI requires robust controls over inter-agent communications and tool calls, including mutual authentication, signed requests, and authorization checks at every step. Comprehensive telemetry and detailed logging of agent activities support auditability and forensic investigations. Organizations should also deploy input/output guardrails and adversarial testing, such as prompt filtering, policy enforcement, output classifiers, and regular red-teaming to uncover vulnerabilities. Whenever models or instructions change, agents should be retested to ensure continued protection. Equally important is conducting structured vendor diligence, with enforceable terms on data handling, retention, breach notification, audit rights, and alignment with recognized AI standards.
- Design for confidentiality, incident response, and responsible operations. Protecting sensitive information means prohibiting the entry of privileged or personal data into open tools and favoring enterprise-licensed, isolated deployments with clear data boundaries. Companies should implement labeling and review workflows to prevent uncontrolled dissemination of confidential outputs and train personnel to recognize and respond to risks, supported by monitoring and data loss prevention measures. Incident response playbooks should be expanded to address agent-specific failures, with tested isolation and rollback procedures and coordinated communications that accurately describe AI use. Finally, disclosures must be aligned with verified capabilities and documented controls to avoid “AI washing” and withstand regulatory scrutiny.
Preparing for Agentic AI’s Next Chapter
The rapid evolution of agentic AI brings both remarkable benefits and complex risks, demanding vigilant governance and proactive security measures. By updating policies, strengthening oversight, and fostering a culture of responsible innovation, companies can ensure their agentic AI deployments remain resilient—no matter how bright or busy the season. With thoughtful preparation, organizations can greet the new year with confidence, knowing their digital lights will stay on and their data secure.