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Generative AI may have dominated the headlines in recent years, but it’s only one part of the AI story unfolding inside enterprises. The next wave, agentic AI, builds on the language and reasoning capabilities of gen AI models and gives them something new: the ability to act, adapt, and decide with minimal human input.
They share a common foundation in machine learning and natural language processing, but their roles in an AI system are fundamentally different. Understanding those differences is critical to making the right investments, setting the right guardrails, and avoiding costly missteps as AI moves from a content generator to an autonomous decision-maker.
In this article, we’ll break down how generative AI and agentic AI compare, where each excels in cybersecurity, and why securing both is non-negotiable for the enterprise.
What is Generative AI?
Generative AI refers to AI tools, often built on large language models, that create new content in response to user prompts. Gen AI models use machine learning and natural language processing to identify patterns and produce text, code, or visuals that match the request. Unlike agentic AI, generative AI is reactive, producing outputs only when prompted.
Core Capabilities of Generative AI
- Content Creation Based on User Prompts: Generates original text, images, code, or other content directly from natural language instructions.
- Pretrained Model-Driven Pattern Generation: Applies knowledge from large-scale training to produce outputs aligned with style, structure, or meaning.
- Multi-Modal Output: Text, Code, Visuals: Delivers diverse formats, from written reports to executable scripts and visual assets.
What is Agentic AI?
Agentic AI refers to AI that can set goals, plan multi-step actions, and execute them with minimal human input. Unlike traditional generative AI, which produces outputs only when prompted, agentic AI operates continuously within defined boundaries, adapting its actions as conditions change. Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI.
Core Capabilities of Agentic AI
- Autonomous Task Execution Without Prompts: Can initiate and complete complex workflows independently, without relying on constant human instructions.
- Real-Time Goal Pursuit and Adjustment: Monitors progress toward objectives and dynamically changes approach based on new inputs, risks, or priorities.
- Feedback Loops for Continuous Improvement: Evaluates its own performance against desired outcomes, learning from successes and failures to optimize future actions.
Agentic AI vs Generative AI: 6 Key Differences
Agentic AI and generative AI serve fundamentally different roles in today’s enterprise. Agentic AI builds on the foundation of gen AI models, adding memory, goals, and autonomy to push beyond content generation and toward decision-making with minimal human input.
More than a mere academic question, the distinction between the two is essential for anyone implementing agentic AI in high-stakes, real-world environments.
Agentic AI Use Cases in Cybersecurity
Agentic AI represents a strategic shift in how security teams operationalize autonomy at scale. While traditional AI models assist with decision-making, agentic AI can actually initiate, adapt, and execute defensive actions in real-time. These autonomous AI agents integrate planning, memory, and tool use—making them ideal for high-volume, high-complexity environments like the modern SOC.
Autonomous Threat Containment and Triage
Instead of waiting for human triage, agentic AI can respond to indicators of compromise the moment they’re detected. These agents function like autonomous incident handlers, capable of orchestrating containment decisions without waiting for analyst approval.
Capabilities include:
- Detecting and classifying threats using behavioral modeling and anomaly detection.
- Evaluating business context (user roles, asset value, sensitivity of data involved).
- Initiating containment actions like revoking credentials, isolating endpoints, or blocking IP ranges.
- Generating forensic summaries for downstream analyst validation.
This shifts containment from hours to milliseconds, without sacrificing control or transparency.
Auto-Remediation of Low-Severity Alerts
Low-severity alerts are noisy, repetitive, and costly to ignore. Agentic AI can fully handle this alert class by autonomously executing remediation playbooks, reducing the need for human intervention.
Common agent actions:
- Auto-revoking user sessions after anomalous logins.
- Revoking temporary access privileges.
- Initiating malware scans or sandbox detonation for flagged files.
- Auto-generating tickets or notifying internal stakeholders only when escalation is needed.
Unlike static automation, agentic systems adjust their actions based on environmental inputs, learning over time which responses are most effective.
Continuous Compliance Enforcement via Policy Agents
Agentic AI agents can act as persistent compliance enforcers, constantly monitoring for drift and policy violations across infrastructure, users, and LLM-based workflows.
How policy agents support continuous compliance:
- Reading and enforcing fine-grained policy logic (e.g., SOC 2, GDPR, HIPAA).
- Scanning LLM interactions for over-permissioned access or out-of-scope data usage.
- Blocking unauthorized plugins, tools, or API access based on contextual rules.
- Triggering internal alerts, remediations, or audits in response to detected violations.
This moves compliance from checklist-based audits to living systems that respond to risk in real time.
Generative AI Use Cases in Cybersecurity
Generative AI still plays a crucial role in augmenting human intelligence, especially in areas that require language synthesis, summarization, and documentation. In high-volume SOC environments, the ability to quickly convert raw signals into actionable insights remains a major bottleneck.
That’s where gen AI models, particularly those trained on security-specific context, offer tangible value.
Real-Time Log Summarization and Anomaly Narration
Security analysts are often buried under thousands of log entries, alerts, and telemetry streams. Generative AI can function as a narrative compression engine, transforming structured and unstructured data into human-readable summaries.
Key capabilities include:
- Translating raw SIEM logs into coherent incident narratives.
- Highlighting anomalous behaviors in plain language.
- Prioritizing events based on threat indicators, user context, and known baselines.
- Feeding enriched summaries into triage queues or ticketing systems.
This drastically reduces time-to-triage and enables junior analysts to operate with greater efficiency.
Drafting Analyst Reports and RCA Documentation
Incident response doesn’t end when a threat is neutralized. It also includes documentation. Generative AI can assist security teams by drafting the first pass of post-incident reports, RCA (Root Cause Analysis) documents, and compliance submissions.
What this enables:
- Structuring incident timelines based on event logs and analyst actions.
- Suggesting risk classifications and regulatory tags (e.g., GDPR, CCPA, HIPAA).
- Creating executive-friendly summaries alongside technical deep dives.
- Automatically linking to playbooks, CVEs, and MITRE ATT&CK mappings.
Analysts retain full control over final edits, but gen AI reduces the time and cognitive overhead of documentation without compromising accuracy.
Auto-Generating Security Playbooks and SOPs
Creating standardized procedures for recurring threats is essential, but time-consuming. Generative AI helps operationalize tribal knowledge by drafting and templating new playbooks based on past incidents, SOC workflows, and threat intelligence feeds.
Use cases include:
- Building Standard Operation Procedures (SOPs) for phishing remediation, insider threat investigations, or malware containment.
- Templating IR workflows based on common attack patterns.
- Enriching static documentation with dynamic links to updated tools or datasets.
- Localizing playbooks across teams, languages, or business units.
In short, generative AI brings speed, consistency, and adaptability to the documentation layer of cybersecurity. This makes the human-machine collaboration tighter and more productive.
Pros and Cons of Agentic AI
Agentic AI Pros
Enables Autonomous Security Workflows
Agentic AI can execute end-to-end security processes like isolating compromised endpoints or initiating forensic scans, without waiting for human approval. This frees SOC teams to focus on higher-order threats.
Real-Time Threat Response
By continuously monitoring logs, network activity, and cloud events, agentic AI agents can detect and act on threats in seconds, dramatically reducing dwell time.
Scales Without Analyst Bottlenecks
Unlike human teams, agentic AI can operate across thousands of assets or tenants simultaneously, scaling security coverage without linear headcount growth.
Reduces Manual Intervention
Automates repetitive or low-level tasks, from routine compliance checks to low-severity alert remediation, minimizing operational fatigue in the SOC.
Agentic AI Cons
Complex Governance
Managing autonomous decision-making at scale requires robust guardrails, explainability tools, and access control policies. Without them, trust in the system erodes.
Risk of Unintended Actions
Without well-defined decision boundaries, an AI agent could take actions that disrupt business, such as revoking critical system access.
High Setup Overhead
Implementing agentic AI involves integrating multiple data sources, defining goals, and building validation layers. These are efforts that demand significant time and expertise.
Requires Operational Alignment
To be effective, agentic workflows must align with incident response procedures, compliance rules, and broader IT governance, requiring cross-team coordination.
While agentic AI and generative AI share a common foundation, their strengths and trade-offs are distinct. What counts as a “pro” for one often exposes a gap in the other.
Pros and Cons of Generative AI
Generative AI Pros
Rapid Content Generation
Generates readable reports, security summaries, and incident narratives in seconds, accelerating documentation and communication tasks.
Adaptable to Various Tasks
Can be fine-tuned or prompted for everything from drafting phishing awareness content to generating synthetic data for model testing.
Boosts Analyst Efficiency
Summarizes logs, extracts relevant threat intelligence, and assists with initial triage, allowing analysts to focus on deeper investigation.
Easy to Integrate
Can be embedded into existing ticketing systems, chat tools, or documentation workflows with minimal engineering effort.
Generative AI Cons
Hallucination Risk
May produce plausible but inaccurate security findings, requiring human validation to avoid acting on false information.
Requires Prompt Tuning
Output quality depends heavily on how prompts are designed, which can lead to inconsistent results without prompt engineering best practices.
No Autonomous Action
Unlike agentic AI, generative AI does not initiate or execute workflows. It only responds when prompted.
Lacks Situational Context
Operates without a persistent awareness of ongoing events or environmental changes, limiting its usefulness in evolving threat scenarios.
Key Risks and Mitigation Strategies in Agentic AI
When AI goes from predicting responses to initiating actions, the attack surface widens dramatically. Unlike traditional gen AI models, agentic AI tools are autonomous actors: they trigger workflows, invoke tools, and make decisions with limited oversight. That power introduces new failure modes stemming from misaligned decisions and untraceable policy drift.
These risks are manageable, but only if organizations build the right control layers into their architecture from day one.
Key Risks and Mitigation Strategies in Generative AI
Generative AI may no longer be the flashiest acronym in the room, especially with autonomous AI agents taking center stage. But its risks haven’t gone anywhere. In fact, as organizations layer agentic capabilities on top of gen AI models, the old vulnerabilities remain just as relevant, and potentially even more dangerous if overlooked.
Before you build autonomous AI agents, you need to lock down the foundation. Here’s where to start.
How Agentic and Generative AI Work Together in Security Operations
In a mature security architecture, generative AI and agentic AI are complementary.
Generative AI handles on-demand synthesis: summarizing incidents, drafting analyst reports, or generating security playbooks.
Agentic AI extends those capabilities into continuous, autonomous action: executing containment steps, enforcing policies, and remediating alerts in real time.
Together, they form a feedback loop: generative AI creates and refines the knowledge base, while agentic AI consumes and acts on it, adapting to new intelligence without waiting for manual intervention.
How Lasso Secures Your Stack With Combined Agentic and Generative AI
Lasso applies context-based access control (CBAC), prompt isolation, and output validation to both gen AI models and AI agents.
This means:
- Autonomous agents execute only within predefined decision boundaries.
- Generative outputs are filtered, logged, and validated before they influence workflows.
- All interactions, reactive or autonomous, are monitored in real time, then acted upon, in line with company-defined policy.
By securing the language layer and the action layer, Lasso ensures AI-powered security operations scale without introducing unmonitored risk.
Conclusion
Generative AI and agentic AI represent different stages of AI maturity in the SOC: one excels at information synthesis, the other at autonomous execution. The real advantage comes from securing and orchestrating both in tandem.
With the right governance, validation, and access controls, enterprises can harness generative AI’s adaptability and agentic AI’s autonomy without sacrificing security or compliance.
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