AI is changing cybersecurity in two directions at once. It gives defenders better tools for detection and response, and it gives attackers more speed, more scale and easier deployment. The most concrete new exposure is AI agents, which are software that reads content from outside its own boundaries and then takes actions. For agents, the central problem is identity: an organization has to know which agent acted, what authority it held, what it did, and whether that authority is governed for the agent’s whole life.
This article keeps four kinds of material apart: draft guidance from NIST, risk descriptions about attackers, standards work that is still in progress, and survey results.
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How is AI changing cybersecurity?
Both effects are real, but the evidence behind each one differs in kind. The defensive gains appear as possibilities in draft guidance. The offensive gains are described as risks.
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NIST’s Cybersecurity Framework Profile for Artificial Intelligence, published as an initial preliminary draft in December 2025, says AI can augment analysts and enhance detection and response. The same draft tells organizations to keep evaluating whether a given capability is mature enough for their needs.
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That second point is the one most often dropped. AI does not make a security function better by default. A tool that helps one team triage alerts can add noise for another, so the practical test is whether a capability performs in your environment, against your workload, before your team depends on it.
Where AI helps attackers
NIST’s draft identifies four ways AI may benefit attackers:
- Speed: steps that take people time to carry out can be compressed.
- Scale: one operator can work on more targets at once.
- Ease of deployment: capabilities become easier to put to use.
- Dynamic optimization: an attack can adjust as it meets defenses.
These are presented as ways AI may benefit attackers, not as measured outcomes. The draft’s description is a risk picture, not an incident statistic.
Are AI cyberattacks getting faster?
The supportable claim is narrower than “AI attacks are fast.” AI can compress the time between attack steps and widen how many targets an attacker can work at once. NIST’s draft lists two potential effects: shorter attack timelines, and difficulty implementing countermeasures within typical timelines.
The evidence does not support the claim that every attacker now operates at machine speed. Attack pace depends on the attacker, the target and the tools in use, and the NIST material cited here does not measure that across the threat landscape.
The practical consequence concerns response windows. If a countermeasure cannot be deployed within the time an attack takes, defense depends on preparation done in advance: current asset inventories, containment steps approved before an incident, and recovery steps that have been rehearsed. This follows from NIST’s concern about countermeasure timelines. It is our inference, not a separate NIST finding.
Why AI agents change the problem
A chat tool that only answers questions has limited reach. An agent reads external data such as email, web pages, documents or tickets, and it acts through tools or applications. If an attacker can place instructions inside that data, the agent may treat them as part of its task.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsNIST’s Center for AI Standards and Innovation (CAISI) described this problem in its January 17, 2025 technical blog, “Strengthening AI Agent Hijacking Evaluations”:
“Currently, many AI agents are vulnerable to agent hijacking, a type of indirect prompt injection in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions.”
Two implications follow. The attacker reaches the agent through content it reads rather than through a login, so controls that only check who is signing in do not address this path. And because attack methods change, NIST stresses adaptive evaluation: an agent that passed an earlier test suite has shown only that it resisted the attacks in that suite.
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What is AI agent identity risk?
AI agent identity risk is the chance that an agent acts in ways no one can attribute, authorize or stop. In practice, a team should be able to answer four questions about each agent:
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- Which agent is acting, and is it one the organization knows about?
- What authority does it hold, and who delegated that authority?
- What did it do, and can the sequence be reconstructed afterward?
- Are its permissions and credentials governed from creation through decommissioning?
NIST treats agent identity as an authorization and accountability problem as much as a model problem. Improving the model does not settle who is answerable for an action an agent took with a credential that several people or agents shared.
Where the standards work stands
Standards activity in this area is recent and still moving. The items below are different kinds of documents, so read each one by its status.
NIST concept paper on software agent identity
NIST’s NCCoE published “New Concept Paper on Identity and Authority of Software Agents” on February 5, 2026. The paper identifies agent identification, authorization, auditing and non-repudiation as areas for standards and best practices. Its public comment period closed April 2, 2026. It describes a proposed project, not a completed certification or a finalized agent identity standard.
NIST’s CSRC item and the August 2026 identity blog
On the same date, February 5, 2026, NIST’s Computer Security Resource Center (CSRC) published a related item titled “Accelerating the Adoption of Software and Artificial Intelligence Agent Identity and Authorization.”
On August 27, 2026, NIST’s Bill Fisher and Ryan Galluzzo published the blog post “Back to the Future: Why Agentic AI Needs a Strong Identity Foundation.” It cautions against credential sharing and discusses registries, credentials, delegated rights, policy management and governance. It states:
“Sharing credentials – between humans or agents – creates accountability gaps that can result in any number of security, privacy, and legal issues.”
A blog post is not binding guidance. This one reports stakeholder feedback and practices, which makes it a signal of NIST’s direction rather than a set of requirements.
Other NIST activity
On February 17, 2026, CAISI announced the AI Agent Standards Initiative for Interoperable and Secure Innovation. On May 18, 2026, NIST published a summary analysis of responses to its request for information on security considerations for AI agents. These show the direction of the work, but they do not by themselves establish requirements an organization must meet.
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The Cloud Security Alliance (CSA) published its findings on April 21, 2026, under the headline “New Cloud Security Alliance Survey Reveals 82% of Enterprises Have Unknown AI Agents in Their Environments.” The reported figures are:
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| Reported finding | Figure | Scope as reported |
|---|---|---|
| Organizations with unknown AI agents in their IT environments | 82% | Surveyed organizations; timeframe not stated; survey commissioned by Token Security |
| Organizations that experienced AI-agent-related incidents | 65% | Surveyed organizations; previous 12 months |
| Organizations with formal AI-agent decommissioning processes | 21% | Surveyed organizations; timeframe not stated |
Taken together, the figures point to a gap between visibility and lifecycle control: most respondents reported agents they did not know about, while few had a formal way to retire agents. That gap is the part most relevant to identity risk.
The survey was commissioned by Token Security, and this article does not establish its sample size or methodology. Treat the percentages as what the survey’s respondents reported. They are not prevalence rates for all organizations, and they should not be compared with other surveys whose populations and questions differ.
How do you secure AI agents?
The priorities below follow the concerns NIST raises: identification, authorization, auditing, credentials and lifecycle, and adaptive evaluation. None of them eliminates prompt injection or cyber risk on its own. Together they limit what a misdirected agent can reach and make its actions visible afterward.
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Give every agent an identifiable identity
Keep an inventory that lists each agent, its owner and the systems it can reach. The survey figures suggest this is where many gaps begin. Check whether you can name every agent with access to production systems, and whether logs tie each action to a specific agent rather than to a shared user account.
Scope authorization and delegation
Grant each agent only the permissions its task requires, and record who delegated them. When an agent acts for a person or for another agent, the delegation should be explicit and revocable. Check whether any agent runs on credentials belonging to a person or to another agent. If it does, NIST’s warning about credential sharing applies directly.
Audit actions and data access
Log what an agent reads, what it sends or changes, and which tool or application it used, with the agent’s own identity on every entry. NIST lists auditing and non-repudiation among its areas for standards work, so the aim is records that show which agent did something and that the action cannot credibly be denied later. Check whether an investigator could reconstruct an agent’s sequence of actions from logs alone.
Manage credentials and the full lifecycle
Rotate or revoke credentials when an agent changes purpose or stops running, and document a decommissioning step that removes its access. This is the lowest figure in the CSA results: 21% of surveyed organizations reported formal decommissioning processes. Check whether a named owner can retire an agent, and whether retirement removes its credentials and permissions.
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Test against changing indirect prompt injection
Evaluate agents with injected instructions placed in the content they are likely to read, such as documents, web pages and tool outputs, not only in direct user prompts. Repeat the tests as attack techniques change. Check whether the test set includes hidden instructions of this kind and is refreshed over time.
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