Encourage responsible AI use
Use AI thoughtfully, with safeguards that fit your organization.
AI Impact Analysis
Make informed decisions about adopting artificial intelligence (AI) with a practical assessment of information exposure, development practices and security controls.
Where do new cybersecurity risks emerge?
Employees may use public assistants, browser extensions and integrated copilots without a shared policy. Prompts, uploaded documents and connected data sources can expose confidential information if access, retention and provider settings are not understood. We map approved and unapproved use with your team, without assuming that every AI tool presents the same risk.
Generated code can introduce insecure patterns, vulnerable dependencies or logic errors. AI-assisted phishing and impersonation can also challenge existing verification procedures. We assess the relevant scenarios for your business, without treating hypothetical attacks as evidence of an incident.
Agentic LLM attack simulation
With a separately agreed scope and written authorization, we can simulate how a rogue or malicious LLM agent might act against your organization. The controlled exercise examines four stages:
- Map the external attack surface.
- Identify possible routes through the perimeter.
- Correlate exposed information and services.
- Track progress toward high-value systems.
We document the attempted routes, the controls encountered and the practical opportunities to interrupt the attack. The simulation uses agreed targets, limits and stop conditions. It does not give an autonomous agent unrestricted access to production systems.
AI impact analysis, step by step
1
Discover actual use
Discover actual use
Agree the business scope, identify tools and owners, map the data shared with providers and review access permissions and integrations.
2
Assess controls
Assess controls
Review acceptable-use rules, information classification, supplier settings, human review of generated output and secure development practices. Technical testing is separately scoped and authorized.
3
Simulate agentic attack paths
Simulate agentic attack paths
For an agentic simulation, agree the targets, limits and stop conditions, then observe how the controlled agent probes the perimeter and combines available information and services into possible attack paths.
4
Prioritize improvements
Prioritize improvements
Connect findings to realistic business consequences and identify proportionate safeguards, accountable owners and practical next steps.
What you receive
A documented AI use and exposure map, a prioritized risk register, control recommendations and an implementation roadmap. The agreed scope determines whether policy updates, developer guidance and a management workshop are included. If the agentic simulation is in scope, we also provide an attack-path map, supporting evidence, control observations and prioritized points where the attack could be interrupted. The assessment supports decision-making.
Business value
Reduce accidental data exposure
Reduce the risk of accidentally sharing sensitive information.
Avoid costly rework
Catch gaps early to avoid costly remediation later.
Frequently asked questions about AI impact analysis
Do we need an internally developed AI system?
No. Employee use of third-party assistants and AI features in existing software can be enough to justify an assessment.
Will you need confidential prompts or production data?
We agree evidence requirements first and prefer redacted examples or controlled test data where possible.
What does agentic mean?
Agentic describes a system that can work toward a goal through several steps. It can choose actions, use available tools, review the result and adjust what it does next instead of only producing a single response.
What is an AI agent?
An AI agent is software that uses an AI model to pursue a defined goal. Depending on its design, it may gather information, call tools, interact with other systems and decide which permitted action to take next.
What does LLM stand for?
LLM stands for large language model. It is a model trained on large amounts of text to understand and generate language, and it can support tasks such as summarization, analysis, conversation and code generation.
What is the difference between AI, machine learning and an LLM?
AI is the broad field of building systems that perform tasks associated with human intelligence. Machine learning is a group of methods through which systems learn patterns from data. An LLM is a type of machine learning model specialized in understanding and generating language.
What does the agentic LLM attack simulation test?
It examines how a controlled agent could identify exposed services, combine available information and attempt routes through the perimeter toward important systems. The goal is to reveal likely attack paths and the controls that can stop them.
Is the agent allowed to operate without limits?
No. We define its targets, permitted actions, technical limits and stop conditions before testing begins. High-impact or destructive actions are excluded.
Does the simulation run against production systems?
The simulation can run against test systems. It produces the most useful information when the scope is broad and includes relevant production systems, because real integrations, identity boundaries and operating controls are visible there. Risk is reduced through continuous human monitoring and oversight, staged actions, predefined limits and the ability to stop the simulation immediately.
Does this replace penetration testing?
No. It identifies AI-related risks and control gaps. The agentic simulation is separately scoped and authorized, and broader penetration testing remains a separate service.