The Fatal Flaw of AI-First Security Agents
AI-first security agents are seductive because they promise to solve cybersecurity's oldest problem: scaling contextual analysis without an endless reliance on human labor.
Connect an LLM to your data repositories, let it ingest your log telemetry, and use its reasoning to generate security verdicts.
Many organizations are exploring this concept, perhaps to their own detriment, because it’s easy to understand. LLMs are genuinely revolutionary at contextual synthesis. They can parse thousands of lines of disparate log data, recognize complex process trees, and assemble clean, narrative root-cause summaries faster than a human analyst.
Yet, the architecture inserts a probabilistic text-generation engine directly into the real-time threat execution loop. The fatal flaw of AI-first solutions is not the technology itself, but where and how the technology is placed within security investigations.
Context vs. Velocity
Security operations are governed by context and velocity, two competing requirements that exist in constant tension:
Context is an analytics problem. It requires deep, narrative reasoning to understand what an alert means.
Velocity is a kinetic physics problem. It requires instantaneous execution to stop an attack.
By forcing raw telemetry into an LLM before a threat is contained, AI-first solutions attempt to solve a velocity crisis with a tool built exclusively for context. It tries to understand the bullet while it’s still flying through the air.
Replacing manual human triage with an agentic processing layer shifts the delay from a human queue to an inference pipeline, and then often back to a human queue when the AI is not fully trusted to take remedial actions.
By forcing raw telemetry into an LLM before a threat is contained, AI-first solutions attempt to solve a velocity crisis with a tool built exclusively for context.
Security incidents follow the same progression: Data must be gathered, meaning must be evaluated, and defense must be executed. Mapping AI agents across three phases — ingestion, inference, and execution — exposes the friction points where context actively destroys velocity. In the era of machine-speed attacks, every misplaced reasoning step becomes a delay the attacker can exploit.
Phase 1: Ingestion into the Alert Queue
All security operations, including agentic solutions, begin at the ingestion phase, where raw event telemetry funnels into the detection pipeline and ultimately to the model's processing environment.
Agentic solutions are beholden to the same ingestion delays that legacy approaches must overcome, typically two to 10 minutes or more from the source of the event, through upstream cloud pipelines and multiple vendors’ infrastructure, before the data is ready for analysis.
By itself, this ingestion delay, which is limited by the physics of data collection, aggregation, and upstream vendors, already provides a window of opportunity for a highly automated and AI-enabled adversary, simply due to the head start the adversary receives.
Phase 2: Inference Introduces a Speed Barrier
Once the data is ingested and a suspicious event is in the alert queue, AI-first solutions move to the inference phase. Queries are generated and tools automatically invoked, packaging raw metadata into several multi-turn agentic prompts, forcing the LLM to compute the context, analyze environmental variables, and generate a conclusive verdict on the validity of the alert, often at the expense of meaningful AI token consumption.
While a single LLM query requires a few seconds to complete, a multi-turn agentic process can take materially longer to complete analysis and yield an actionable verdict.
The inference phase is governed by an absolute physical constraint: the adversary’s timeline.
Modern attack toolkits are fully automated. The moment a modern threat actor establishes initial access on an endpoint, they do not pause to plan. They execute internal reconnaissance, harvest local credentials from memory or configuration files, and begin lateral movement in as little as 27 seconds.
At its core, the use of AI for all investigations is like using ChatGPT to calculate the multiplication of 3,479 times 7.671.
An agentic approach introduces a dangerous computational delay. By the time the model finishes processing its token sequence, generating a verdict and a response plan, the AI-enabled adversary has already had the opportunity to clear the initial asset, spread laterally, and establish persistence elsewhere on the network, sending new detection signals and alerts into the pipeline to be subsequently analyzed — before the first detections are even complete.
A simple detection just became an order of magnitude more complex.
At its core, the use of AI for all investigations is like using ChatGPT to calculate the multiplication of 3,479 times 7.671. AI can do it, but clearly not as fast, efficient, or as cheap as a simple deterministic calculator app.
Phase 3: Execution Paralyzes the Response Loop
The final phase of the lifecycle is execution, the literal point of containment. Security decisions are strictly binary at this layer. An administrative terminal command is either authorized or malicious. A questionable login from a privacy VPN is either an employee or an imposter. A production server is either clean or compromised. Neutralizing a machine-speed threat requires binary, zero-delay execution.
However, LLMs are fundamentally probabilistic engines. They don’t operate on binary truths. They calculate statistical likelihoods based on historical training data and their model’s bias, creating an acute operational paralysis at the exact moment containment is required.
The automation stalls, the alert is boomeranged back to a human, and the time advantage is handed completely to the attacker.
If AI analyzes a suspicious script on a critical database server and returns an 85% confidence score that it represents a breach, the system faces a dilemma. It must either trigger a highly disruptive host isolation based entirely on a statistical guess, or pause the pipeline and route the alert to a human queue for manual verification.
Because the business cost of a false-positive automated shutdown is high, buyers of probabilistic systems typically default to hesitation before taking disruptive action. The automation stalls, the alert is boomeranged back to a human, and the time advantage is handed completely to the attacker.
Right Tool, Right Place: Automated Detection & Response
The single most expensive way, in terms of both time and financial costs, to investigate alerts is with an army of humans. The second most expensive way is with AI because investigation consumes model calls, tokens, and orchestration. The least expensive, when it can be known and implemented, is with prebuilt deterministic investigation flows.
Defending a network at machine speed is the art of knowing when to blend the three: humans, AI, and determinism. This is the structural framework of automated detection and response (ADR).
Determinism: Rather than reinvent the wheel for each alert in the queue by asking AI to come up with an investigation plan on the fly, Wirespeed ADR uses its taxonomy to map the detection into its class and category. Wirespeed ADR then knows precisely which enrichments, queries, and hunts to identify malicious threats, invoking AI only in specific instances where experts knew in advance that additional context would be needed. This is like using the calculator app instead of asking ChatGPT to compute the math for you.
AI: Instead of consuming extra LLM tokens for every custom SIEM detection, Wirespeed ADR uses AI to analyze the rule’s logic before it fires, classify it in the Wirespeed taxonomy, and then every subsequent match to the rule is already categorized and investigated in milliseconds. When context can’t be pre-known, such as evaluating process trees with user identity during a live off the land attack, Wirespeed ADR invokes AI to classify the detection.
Humans: While other security offerings go immediately to agentic multi-turn investigations, Wirespeed ADR saves that as a follow-on option for inquisitive humans to go deeper after Wirespeed reaches its verdicts in a lightning-fast median time of 754 milliseconds.*
This hybrid approach is designed to help security teams investigate all alerts with a high degree of confidence, predictable costs, and the lightning-fast speeds needed to combat the highly-automated, modern adversaries of the future.
Security teams shouldn’t have to choose between context and velocity. By limiting probabilistic models to only the places where it is the best tool, Wirespeed ADR reduces the validation queue and stops the threat before the inference loop even begins.
LIGHTNING-FAST SPEED. LASER PRECISION.
Wirespeed Automated Detection & Response
Start your free 30-day trial >