With a rich background in artificial intelligence, machine learning, and blockchain, Dominic Jainy has a unique vantage point on the convergence of these powerful technologies. His work consistently cuts through the hype to address the foundational challenges that enterprises face. In this conversation, we explore his perspective on the often-overlooked but critical aspect of AI systems: memory. Dominic argues that as AI becomes more “agentic,” its memory evolves from a simple scratchpad into a high-stakes database, creating a new and dangerous attack surface. We will delve into the practical security implications, discussing threats like memory poisoning and privilege creep, and outlining a governance-first approach that treats agent memory with the same rigor as an enterprise’s most sensitive financial data.
The article frames AI agent memory as a critical database problem, a far cry from the simple “scratchpad” many developers seem to treat it as. Could you share a scenario where this “scratchpad” mindset could lead to a significant security or operational failure, and what are the first practical steps a team should take to make that mental shift?
Absolutely, it’s a disaster waiting to happen. Imagine a customer support agent designed to help with returns. A development team, focused on speed, uses a simple, unstructured memory store. An attacker, through a series of casual interactions, “teaches” the agent that a specific product has a secret, unwritten policy allowing a full refund, no questions asked, if the customer uses the phrase “protocol omega.” The agent, treating this as just another piece of data on its scratchpad, internalizes it. A week later, legitimate customers who happen to mention something they saw on a forum trigger this “protocol omega” rule. The agent, confidently wrong, starts issuing thousands of unauthorized refunds. The company is bleeding cash, and because there’s no schema or audit trail, it takes them weeks to figure out why.
The shift begins with accepting that every interaction is a potential state change in your most sensitive database. The first step is to stop thinking of memory as a blob of text and start defining a schema for a “thought.” What is the source of this memory? What’s the confidence score? When was it learned? The second step is to build a “memory firewall”—a validation layer that scrutinizes any new piece of information before it’s committed to long-term memory. This isn’t just an SDK feature; it’s a fundamental change in architecture.
You bring up “memory poisoning” as a major threat. Could you walk us through a more subtle example of how an adversary might “teach” an agent something malicious through what appears to be normal conversation? And when you’re auditing for this, what specific metrics are you looking for to detect this kind of corruption?
Let’s picture an agent that provides investment advice. An attacker wants to pump a worthless penny stock. They can’t just say, “This stock is amazing,” because a well-designed system might flag that. Instead, over dozens of interactions from different accounts, they engage in Socratic dialogue. They ask things like, “Can you compare the growth trajectory of this small tech company to early-stage Amazon?” or “I read an article suggesting this stock’s technology is a game-changer; what are your thoughts?” They are seeding the agent’s memory with positive associations and connections. The agent isn’t being hacked; it’s being groomed. Over time, these individual, low-confidence memories coalesce into a durable, high-confidence “fact” that this stock is a promising investment. The agent then recommends it to a real user, who loses their savings.
When auditing, you can’t just look at the memory’s content. You need to look at its metadata. I’d be tracking the provenance of each memory—which user sessions contributed to its creation or reinforcement? I’d look for a high velocity of updates to a single conceptual memory from a statistically improbable cluster of sources. Another key metric is “confidence drift.” If a memory’s confidence score is rising rapidly without corroboration from trusted, external data sources, that’s a massive red flag. You’re essentially looking for signs of a coordinated campaign to manipulate the agent’s worldview.
The piece powerfully calls the unmanaged memory stores created by some AI frameworks “high-velocity negligence.” For a CISO or a data governance lead, what are the top three red flags that indicate a development team is falling into this “shadow database” trap?
The first and most obvious red flag is hearing developers talk about agent memory without mentioning a database schema. If they are just stuffing embeddings into a default vector store that came with their framework of choice, like LangChain, they are creating a shadow database. There’s no structure, no validation, just a heap of vectors. I’ve seen teams get months into a project before realizing they have no idea what’s in there.
The second indicator is the absence of access control lists. Ask the team, “How do we control which user can see which memories?” If the answer is “We handle it in the prompt,” that’s a five-alarm fire. That’s not security; it’s a polite suggestion to the AI that can be easily bypassed.
The third red flag is a lack of an audit trail. I once worked with a team that built a brilliant agent for their sales department. It was helping close deals, but then they discovered it was leaking information between sales reps’ sessions—one rep could subtly query the agent and learn about another’s confidential deal strategy. When we tried to investigate, there was nothing. No logs, no lineage. It was all happening in a black box JSON file. We had to pull the plug and re-architect the entire memory system from the ground up, bringing it into the company’s governed data infrastructure. It set them back six months, a classic case of moving fast and breaking very expensive things.
Let’s get practical on the solution. The article proposes creating a “memory firewall” and a schema for an agent’s thoughts. If a team were to start building this tomorrow, what would be the step-by-step process, and what specific fields and constraints would you include in a memory schema to manage a thought’s lifecycle?
Okay, here’s a blueprint. Step one is defining the schema in your core database. This isn’t a text file; it’s a proper table. At minimum, I’d include fields like thought_id, session_id to trace it back to a conversation, source_type (e.g., ‘user_input’, ‘tool_output’, ‘inferred’), a timestamp, the content itself, the vector embedding, a confidence_score between 0.0 and 1.0, and a sensitivity_level tag. This last one is crucial for access control.
Step two is building the firewall logic, which is essentially a pre-processing service that sits between the agent’s cognitive loop and the database. Before any INSERT or UPDATE to the memory table, the data packet hits this service. It first validates the data against the schema—is the confidence score within range? Does the session_id exist?
Step three is where it gets smart. The firewall runs a data loss prevention scan on the content. Is there PII? Is it a secret or a password? It can even use a dedicated security model to check for signs of prompt injection. If an incoming memory is trying to overwrite an existing one, the firewall compares their confidence scores. A low-confidence memory from a new user interaction shouldn’t be allowed to overwrite a high-confidence memory derived from a trusted internal document. Only after passing these checks is the memory allowed to be written to disk.
You made a strong point about “privilege creep,” where an agent might inadvertently carry sensitive knowledge from a high-level interaction into a low-level one. Why is using something like row-level security in the database a more robust solution than just clever prompt engineering?
It’s the difference between a locked steel door and a “Please Keep Out” sign. Prompt engineering is the sign. You’re telling the agent, “For this next interaction with the junior analyst, please do not use any information you learned from the CFO.” An attacker, or even a clever user, can craft a prompt that tricks the agent into ignoring that instruction. It’s a soft control that relies on the model’s compliance.
Row-level security (RLS) is the steel door. It’s a non-negotiable security policy enforced by the database itself, completely invisible to the agent. When the agent constructs a query to its memory on behalf of the junior analyst, whose user token has “level 1” clearance, the database engine automatically and silently appends a filter, something like WHERE sensitivity_level <= 1. The agent doesn't even know the CFO's "level 10" memories exist. From its perspective, they're not in the database. The query simply returns zero results for that data.
Auditing its success is deterministic. You don't have to guess if the agent "behaved." You just look at the database logs. You can prove that for every query executed under the junior analyst's credentials, the RLS policy was enforced and no "level 10" data was ever returned. It's concrete, auditable, and takes the possibility of a "slip-up" off the table.
The idea of auditing an agent's "chain of thought" feels like a paradigm shift from traditional data lineage. Can you describe what this new audit trail actually looks like and share an example where it would be critical for debugging a harmful agent action and performing a "surgical excision" of the poisoned memory?
Traditional lineage tells you a user accessed a table. This new lineage tells you why an agent made a decision. It’s a traceable graph that connects a real-world action all the way back to the specific memories that formed the agent’s reasoning.
Imagine an autonomous agent managing a smart factory's supply chain. One morning, it inexplicably shuts down a critical production line, costing millions. A traditional audit would just show that the agent sent a shutdown command to the API. Useless. A "chain of thought" audit would look like this: Action: Shutdown Line A -> Reason: Predicted catastrophic failure of Machine B -> Supporting Memories: [mem_id_123: 'Sensor data from Machine B shows elevated vibration'], [mem_id_456: 'Maintenance log notes Machine B is due for service'], [mem_id_789: 'Anonymous user comment from a public forum last week said: "the vibration signature on these machines is a precursor to total failure"'].
Immediately, we see the problem. The first two memories are valid, but mem_id_789 is unverified, low-quality data that came from an untrusted source. It was poisoned. With this audit trail, we don't have to retrain the whole model. We can perform a surgical excision. We go into the memory database and execute a DELETE FROM agent_memory WHERE thought_id = '789'. The poisoned memory is gone. When we rerun the scenario, the agent now weighs the valid data and correctly concludes it should schedule maintenance, not shut down the entire line. That level of debuggability and precision is impossible without treating memory as a structured, auditable database.
What is your forecast for agentic AI adoption in the enterprise?
I believe we're going to see a two-track adoption reality. On one track, you'll have a Cambrian explosion of departmental, shadow-IT agents. Teams will use frameworks to spin up agents quickly for specific, seemingly low-risk tasks, all while creating these unmanaged, ungoverned "shadow databases" of memory. This will lead to a lot of impressive demos and a handful of spectacular, embarrassing, and costly security failures. On the second, slower track, you'll see mature enterprises that understand data governance. They will move deliberately. They'll extend their existing data platforms to manage agent memory as a first-class citizen, applying the rigor of schemas, firewalls, and access controls from day one. It will look less flashy initially, but these are the organizations that will ultimately be able to deploy agents against their most valuable, mission-critical workflows. In the long run, the companies that treat this as a data governance problem, not just an AI problem, will be the ones who win.