We examine trust boundaries, identities, prompts, retrieval, data, models, tools, permissions, side effects, and recovery. Some engagements map the risk, some test it, and some retest a changed path. Each page states which evidence and decision it is responsible for.

Some of the 500+ brands we've worked with. Our delivery runs on 100+ AI workflows in production.

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Choose from 6 security boundaries to examine, spanning threat modeling, architecture, prompt attacks, agents, RAG, and red teaming.

Assure & Operate

AI ThreatModeling

We follow data, models, tools, identities, permissions, and people across the points where trust changes. Credible attacker goals and misuse cases then lead to a control, a test someone can run, an accountable owner, and a condition for reopening the risk.

Start with threat modeling while the AI architecture can still move, mapping trust changes to attacker paths, owned controls, runnable tests, and review triggers.

Prompt Injection &Jailbreak Testing

An instruction may arrive in a user message or sit inside a document the system retrieves. We follow both routes through the application, prompts, policies, tools, permissions, protected data, and downstream actions. Each verified bypass comes with a controlled trace and a retest that includes ordinary valid requests.

Pick this path when untrusted text arrives through user messages or retrieved documents and could still reach a tool, protected data, or a downstream action.

AI RedTeaming

We test how an adversary could combine weaknesses across the model, application, data, tools, users, and operations. You approve the campaign and its stop conditions first. Findings must reproduce before they enter the risk record, and agreed fixes face the same paths again.

Red teaming fits an authorized campaign that combines model, application, data, tool, user, and operations weaknesses and retests reproduced findings.

Secure AIArchitecture Review

Architecture diagrams often state that a boundary is isolated, logged, or recoverable. We follow those claims across identities, data, models, and tools, then compare them with the evidence available for one agreed design state. Open exceptions return with owners and the checks required for closure.

Choose the review when a design claims isolation, logging, or recovery and someone must weigh those claims against evidence for one agreed version.

Agent, Tool &MCP Security Testing

An agent may look bounded in a prompt and still hold a tool permission that changes the outcome. We test identities, MCP connections, tool contracts, grants, instruction boundaries, and side effects as one system. Reproducible findings return to your security owner for exception and release decisions.

Test the agent boundary when identities, MCP connections, tool contracts, and grants were each approved separately and the resulting action path was never exercised.

RAG Security, Data Exfiltration& Poisoning Testing

A RAG answer carries the security decisions made at the source, access filter, ingestion step, retrieval path, output, and citation. We exercise that chain for poisoning, manipulation, and exfiltration within an authorized boundary. Findings stay linked to their evidence, owner, exception decision, and retest.

RAG security testing fits when source trust, access filters, ingestion, retrieval, output, and citations need one authorized poisoning and exfiltration boundary.

An AI path can cross prompts, retrieved content, identities, data, tools, third parties, and actions the application is allowed to take. Reviewing one component in isolation can leave the consequential boundary unseen.

We agree the protected assets, adversary model, plausible abuse paths, safety limits, evidence handling, stop conditions, and person with authority to accept the result before any test begins. Scope changes return to that authorization instead of being decided during execution.

Protected assets, adversary model, safety limits, and stop conditions are agreed before any test begins.

We agree the protected assets, system state, adversary view, permitted access, plausible abuse paths, evidence handling, stop conditions, and the person with authority to accept the result, then establish the baseline from current architecture, data, permissions, prompts, tools, controls, incidents, and dependencies, leaving unknowns marked as unknown. The agreed review or test path runs inside the approved boundary and is assessed against stated thresholds; a scope change returns to authorization instead of being decided mid-execution. Where the engagement includes retesting, changed paths run again, and the handoff records what closed, what remains open, and what would trigger another review.

The parent-level path from scoped question to accepted evidence

Set the rules and authority
We agree the assets, system state, adversary view, permitted access, evidence handling, stop conditions, and person authorized to act on the result.
Establish what is known
Current architecture, data, permissions, prompts, tools, controls, incidents, and dependencies form the baseline, while unknowns remain marked as unknown.
Exercise the agreed security question
The agreed review or test path runs inside the approved boundary, with evidence and reproduced behavior where applicable assessed against the stated thresholds.
Retest and hand the choice back
Where the selected task includes retesting, changed paths run again and the handoff records what closed, what remains open, and what triggers another review.

Every operational consultant at Zeo has secure LLM access and training, and AI sits inside the daily work. Five of them came through our AI Bootcamp and wrote down what they expect it to change.

I see generative AI having an enormous effect on daily life and on every industry it touches. As the technology develops, the range of uses will keep widening across creativity, problem-solving, and innovation. We can already see that range in realistic image, video, and music production, pharmaceutical research, and design. I expect the effect on industries to become profound. E-commerce, healthcare, finance, and many other sectors will be able to create more engaging, personalized experiences and make their processes more efficient.

The ability to produce unique content and solutions will open new possibilities and increase efficiency.

Ozan Ketenci

Generative AI has the potential to transform SEO, digital marketing, and many other sectors. I expect it to play an important role in our lives in the near future, with more personal experiences, more effective marketing, faster interpretation of data, and quicker action. Products and services will improve. Processes such as customer communication will become more efficient, and organizations that fail to keep up will fall behind businesses that bring AI into their work.

Organizations should start planning the AI applications that make sense for their sector now.

Samet Özsüleyman

We may be at a moment as significant as the computer revolution, with the potential to transform businesses and industries. Yet for many people, generative AI still means opening a tool such as ChatGPT for a task at work or in daily life. That is only the surface. Companies that integrate generative AI models into workflows and customer processes, and go beyond content production, will gain huge competitive advantages in the coming years.

I believe generative AI should be on the agenda of every board of directors as soon as possible.

Hande Parmaksız

I see artificial intelligence as the most exciting technology of both the present and the future. Its potential is unlimited, and we're still at the tip of the iceberg. AI is developing quickly, while much of what it could mean for different sectors remains unexplored. The effect on digital work is already substantial. In the years ahead, I expect breakthroughs that change how entire industries work.

AI's potential will keep expanding. No sector can afford to ignore the opportunity for efficiency and progress. We will keep discovering new dimensions, and I don't see a saturation point.

Can Mutioğlu

Work by major technology companies is likely to give generative AI a much wider role in the years ahead. It will create new dynamics in art and design, as well as in sensitive fields such as healthcare and finance. As the technology becomes part of daily life, the ethical and risk questions will grow with it. Being able to follow and experience those developments up close is what makes generative AI so exciting to me.

I look forward to seeing more uses of generative AI that benefit society.

Ezgi Gülsen Yaylı

Three speakers look at the pace of AI change and what it means for e-commerce and content teams.

The Future Will Come Faster Than You Imagined

Yiğit Konur, Wope

Effective Use of AI for E-Commerce Companies

Burak Pehlivan, Wope

Producing Content Compatible with Google Algorithms with AI

Mert Erkal, Stradiji

Agents, chatbots, and RAG systems at Zeo are built by senior engineers who keep operating them after launch. The consultants below are those builders, matched to the work this page covers.